-
L. Wanner, R. Baeza-Yates, S. Brugmann, J. Codina, B. Diallo, E. Escorsa, M. Giereth, Y. Kompatsiaris, S. Papadopoulos, E. Pianta, G. Piella, I. Puhlmann, G. Rao, M. Rotard, P. Schoester, L. Serafini, and V. Zervaki, "Towards content-oriented patent document processing," World Patent Information, vol. 30, iss. 1, pp. 21-33, 2008.
@article{wanner_towards_2008, title = {Towards content-oriented patent document processing},
volume = {30},
url = {http://www.sciencedirect.com/science/article/B6V5D-4NT93N4-1/2/28ca16a1b7ef2db1ffd30377e0b37df4},
abstract = {In this article, we present ongoing work on an advanced patent processing service {PATExpert.} The central assumption underlying {PATExpert} is that in order to meet the needs of the users of patent processing services, recourse must be made to the content of patent material. We introduce a content representation schema for patent documentation and sketch the design of techniques that facilitate the integration of this schema into the patent processing cycle. Two types of techniques are discussed. Techniques of the first type facilitate the access to the content of patent documentation provided in a textual format - be it by the human reader or by the machine - in that they rephrase and summarize the documentation and map it onto a formal semantic representation. Techniques of the second type operate on the content representation. At this stage, {PATExpert} is explored in two technology areas - optical recording devices and machine tools. The work is being carried out in the framework of an {R\&D-project} partially funded by the European Commission.},
number = {1},
journal = {World Patent Information},
author = {Leo Wanner and Ricardo {Baeza-Yates} and Soren Brugmann and Joan Codina and Barrou Diallo and Enric Escorsa and Mark Giereth and Yiannis Kompatsiaris and Symeon Papadopoulos and Emanuele Pianta and Gemma Piella and Ingo Puhlmann and Gautam Rao and Martin Rotard and Pia Schoester and Luciano Serafini and Vasiliki Zervaki},
year = {2008},
keywords = {Classification, Extraction d'information, Ontologie, Visualisation de l'information},
pages = {21--33} },
-
B. K. Sarker, P. Wallace, and W. Gill, "Some observations on mind map and ontology building tools for knowledge management," Ubiquity, vol. 9, iss. 9, pp. 1-9, 2008.
@article{sarker_observationsmind_2008, title = {Some observations on mind map and ontology building tools for knowledge management},
volume = {9},
url = {http://portal.acm.org/ft_gateway.cfm?id=1353570&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1353568.1353570},
abstract = {Ontology is a fundamental data object for organizing knowledge in a structured way in many areas ranging from philosophy to Knowledge Management. Knowledge capture, knowledge integration and knowledge delivery are the essential parts of dynamic knowledge management. {E-Learning} is considered to be an integral part of knowledge delivery system. Information architect plays an important role in developing the system, and are primarily responsible for capturing and modeling knowledge from various Information sources as a part of {eLearning.} Ontology is found to be useful and efficient as a basis to capture the knowledge, model it in a structured way and disseminate it for further processing from various information sources. In this paper, we present a brief description on the role of ontology in e-learning and review the ontology building tools. The purpose for reviewing ontology building tools is to determine the toolkit most suitable for ontology creation, editing, and mind/concept mapping from the view points of Information Architects {(IAs)} who play a significant role in designing knowledge management systems. The paper also gives a fundamental understanding of ontology tools available on the market as open source products as well as commercial products in terms of their capability, availability, enhancement and further development. We provide a ranked list of the tools based on our needs and suitability for the {IAs.}},
number = {9},
journal = {Ubiquity},
author = {Biplab K. Sarker and Peter Wallace and Will Gill},
year = {2008},
keywords = {Gestion des connaissances, Ontologie},
pages = {1--9},
annote = {{{\textless}p{\textgreater}sarkerBiplab2008.pdf{\textless}/p{\textgreater}}} },
-
F. Wu and D. S. Weld, "Automatically refining the wikipedia infobox ontology," , Beijing, China, 2008, pp. 635-644.
@inproceedings{wu_automatically_2008, address = {Beijing, China},
title = {Automatically refining the wikipedia infobox ontology},
isbn = {978-1-60558-085-2},
url = {http://portal.acm.org/ft_gateway.cfm?id=1367583&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1367497.1367583},
abstract = {The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces {KOG,} an autonomous system for refining Wikipedia's infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both {SVMs} and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia's infobox-class schemata with {WordNet.} We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.},
publisher = {{ACM}},
author = {Fei Wu and Daniel S. Weld},
year = {2008},
keywords = {Ontologie},
pages = {635--644},
annote = {{{\textless}p{\textgreater}wuFei2008.pdf{\textless}/p{\textgreater}}} },
-
L. Zhou and P. Chaovalit, "Ontology-supported polarity mining.," Journal of the American Society for Information Science \& Technology, vol. 59, iss. 1, pp. 98-110, 2008.
@article{zhou_ontology-supported_2008, title = {Ontology-supported polarity mining.},
volume = {59},
issn = {15322882},
url = {http://portal.acm.org/ft_gateway.cfm?id=1282314&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {Article},
abstract = {Polarity mining provides an in-depth analysis of semantic orientations of text information. Motivated by its success in the area of topic mining, we propose an ontology-supported polarity mining {(OSPM)} approach. The approach aims to enhance polarity mining with ontology by providing detailed topic-specific information. {OSPM} was evaluated in the movie review domain using both supervised and unsupervised techniques. Results revealed that {OSPM} outperformed the baseline method without ontology support. The findings of this study not only advance the state of polarity mining research but also shed light on future research directions. {ABSTRACT} {FROM} {AUTHOR} Copyright of Journal of the American Society for Information Science \& Technology is the property of John Wiley \& Sons, Inc. / Business and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. {(Copyright} applies to all Abstracts)},
number = {1},
journal = {Journal of the American Society for Information Science \& Technology},
author = {Lina Zhou and Pimwadee Chaovalit},
year = {2008},
keywords = {Ontologie},
pages = {98--110},
annote = {{{\textless}p{\textgreater}Accession} Number: 28548763; Zhou, Lina 1; Chaovalit, Pimwadee 1; Affiliations: 1: Department of Information Systems, {UMBC,} 1000 Hilltop Circle, Baltimore, {MD} 21250; Issue Info: Jan2008, Vol. 59 Issue 1, p98; Subject Term: {POLARITY} {(Linguistics);} Subject Term: {ONTOLOGIES} {(Information} retrieval); Subject Term: {TEXT} mining {(Information} retrieval); Subject Term: {OPINION} {(Philosophy);} Subject Term: {DATA} extraction; Subject Term: {AUTOMATIC} classification; {Author-Supplied} Keyword: automatic classification; {Author-Supplied} Keyword: ontologies; {Author-Supplied} Keyword: polarity; {Author-Supplied} Keyword: sentiment analysis; {Author-Supplied} Keyword: text mining; Number of Pages: 13p; Document Type: Article{\textless}/p{\textgreater}} },
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T. Morita, N. Fukuta, N. Izumi, and T. Yamaguchi, "DODDLE-OWL : interactive domain ontology development with open source software in Java," IEICE Transactions on information and systems, vol. E91D, iss. 4, pp. 945-958, 2008.
@article{morita_doddle-owl_2008, title = {{DODDLE-OWL} : interactive domain ontology development with open source software in Java},
volume = {{E91D}},
issn = {0916-8532},
shorttitle = {{DODDLE-OWL}},
abstract = {In this paper, we propose an interactive domain ontology development environment called {DODDLE-OWL.} {DODDLE-OWL} refers to existing ontologies and supports the semi-automatic construction of taxonomic and other relationships in domain ontologies from documents. Integrating several modules, {DODDLE-OWL} is a practical and interactive domain ontology development environment. In order to evaluate the efficiency of {DODDLE-OWL,} we compared {DODDLE-OWL} with popular manual-building method. In order to evaluate the scalability of {DODDLE-OWL,} we constructed a large sized ontology over 34,000 concepts in the field of rocket operation using {DODDLE-OWL.} Through the above evaluation, we confirmed the efficiency and the scalability of {DODDLE-OWL.} Currently, {DODDLE-OWL} is open source software in Java and has 100 and more users from 20 and more countries.},
number = {4},
journal = {{IEICE} Transactions on information and systems},
author = {T Morita and N Fukuta and N Izumi and T Yamaguchi},
month = apr, year = {2008},
keywords = {Ontologie},
pages = {945--958} },
-
D. Thorleuchter, "Finding new technological ideas and inventions with text mining and technique philosophy," in Data analysis, machine learning and applications : proceedings of the 31st annual conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, march 7–9, 2007, 2008, pp. 413-420.
@inproceedings{thorleuchter_finding_2008, series = {Studies in classification, data analysis, and knowledge organization},
title = {Finding new technological ideas and inventions with text mining and technique philosophy},
url = {http://dx.doi.org/10.1007/978-3-540-78246-9_49},
abstract = {Text mining refers generally to the process of deriving high quality information from unstructured texts. Unstructured texts come in many shapes and sizes. It may be stored in research papers, articles in technical periodicals, reports, documents, web pages etc. Here we introduce a new approach for finding textual patterns representing new technological ideas and inventions in unstructured technological texts. This text mining approach follows the statements of technique philosophy. Therefore a technological idea or invention represents not only a new mean, but a new purpose and mean combination. By systematic identification of the purposes, means and purpose-mean combinations in unstructured technological texts compared to specialized reference collections, a (semi-) automatic finding of ideas and inventions can be realized. Characteristics that are used to measure the quality of these patterns found in technological texts are comprehensibility and novelty to humans and usefulness for an application.},
booktitle = {Data analysis, machine learning and applications : proceedings of the 31st annual conference of the Gesellschaft für Klassifikation {e.V.,} {Albert-Ludwigs-Universität} Freiburg, march 7–9, 2007},
publisher = {Springer},
author = {Dirk Thorleuchter},
year = {2008},
keywords = {Fouille de texte, Philosophie},
pages = {413--420} },
-
A. S. Kleshchev and E. A. Shalfeeva, "Defining structural properties of ontologies," Journal of Computer and Systems Sciences International, vol. 47, iss. 2, pp. 226-234, 2008.
@article{kleshchev_defining_2008, title = {Defining structural properties of ontologies},
volume = {47},
number = {2},
journal = {Journal of Computer and Systems Sciences International},
author = {A. S. Kleshchev and E. A. Shalfeeva},
year = {2008},
keywords = {Ontologie},
pages = {226--234} },
-
J. Diesner and K. Carley, "Conditional random fields for entity extraction and ontological text coding," Computational \& Mathematical Organization Theory, 2008.
@article{diesner_conditional_2008, title = {Conditional random fields for entity extraction and ontological text coding},
url = {http://dx.doi.org/10.1007/s10588-008-9029-z},
doi = {10.1007/s10588-008-9029-z},
abstract = {Abstract Previous research suggests that one field with a strong yet unsatisfied need for automatically extracting instances of various entity classes from texts is the analysis of socio-technical systems {(Feldstein} in Media in Transition {MiT5,} 2007; Hampe et al. in Netzwerkanalyse und Netzwerktheorie, 2007; Weil et al. in Proceedings of the 2006 Command and Control Research and Technology Symposium, 2006; Diesner and Carley in {XXV} Sunbelt Social Network Conference, 2005). Traditional as well as non-traditional and customized sets of entity classes and the relationships between them are often specified in ontologies or taxonomies. We present a Conditional Random Fields {(CRF)-based} approach to distilling a set of entities that are defined in an ontology originating from organization science. {CRF,} a supervised sequential machine learning technique, facilitates the derivation of relational data from corpora by locating and classifying instances of various entity classes. The classified entities can be used as nodes for the construction of socio-technical networks. We find the outcome sufficiently accurate (82.7 percent accuracy of locating and classifying entities) for future application in the described problem domain. We propose using the presented methodology as a crucial step in the process of advanced modeling and analysis of complex and dynamic networks.},
journal = {Computational \& Mathematical Organization Theory},
author = {Jana Diesner and Kathleen Carley},
year = {2008},
keywords = {Fouille de texte, Ontologie} },
-
Z. Yang, C. Cheng, and Z. Feng, "Construction of ontology-based safety assessment system for power plants," Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on, pp. 1092-1096, 2008.
@article{yang_construction_2008, title = {Construction of ontology-based safety assessment system for power plants},
journal = {Networking, Sensing and Control, 2008. {ICNSC} 2008. {IEEE} International Conference on},
author = {Z. Yang and C. Cheng and Z. Feng},
year = {2008},
keywords = {Ontologie},
pages = {1092--1096} },
-
D. Sanchez and A. Moreno, "Learning non-taxonomic relationships from web documents for domain ontology construction," Data \& Knowledge Engineering, vol. 64, iss. 3, pp. 600-623, 2008.
@article{sanchez_learning_2008, title = {Learning non-taxonomic relationships from web documents for domain ontology construction},
volume = {64},
issn = {{0169-023X}},
abstract = {In recent years, much effort has been put in ontology learning. However, the knowledge acquisition process is typically focused in the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected, even though it is a fundamental point in structuring domain knowledge. This paper presents an automatic and unsupervised methodology that addresses the non-taxonomic learning process for constructing domain ontologies. It is able to discover domain-related verbs, extract non-taxonomically related concepts and label relationships, using the Web as corpus. The paper also discusses how the obtained relationships can be automatically evaluated against {WordNet} and presents encouraging results for several domains. (c) 2007 Elsevier {B.V.} All rights reserved.},
number = {3},
journal = {Data \& Knowledge Engineering},
author = {D Sanchez and A Moreno},
month = mar, year = {2008},
keywords = {Document numérique, Ontologie},
pages = {600--623} },
-
H. Kim, J. Hwang, B. Suh, Y. Nah, and Hyung-Soo, "Semi-automatic ontology construction for visual media web service," , Suwon, Korea, 2008, pp. 69-73.
@inproceedings{kim_semi-automatic_2008, address = {Suwon, Korea},
title = {Semi-automatic ontology construction for visual media web service},
isbn = {978-1-59593-993-7},
url = {http://portal.acm.org/ft_gateway.cfm?id=1352808&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1352793.1352808},
abstract = {Ontology is a description of the concepts and relationships that can exist for an agent or a community of agents. Ontologies are a key enabling technology for the Semantic Web. Previously, ontologies have been built manually by knowledge engineers using ontology editors. In this paper, we propose a semiautomatic ontology construction scheme for visual media data brokers and providers. We apply data mining techniques on sets of image data groups to determine general term relationships and utilize {WordNet} to determine semantic relationships between terms. Even though our research has been started for visual media data retrieval, the proposed scheme can be also applied to other problem domains.},
publisher = {{ACM}},
author = {Hayoung Kim and Jaeil Hwang and Bowon Suh and Yunmook Nah and {Hyung-Soo} Mok},
year = {2008},
keywords = {Ontologie, Recherche d'information, Visualisation de l'information},
pages = {69--73},
annote = {{{\textless}p{\textgreater}kimHayoung2008.pdf{\textless}/p{\textgreater}}} },
-
E. Blomqvist, "Pattern ranking for semi-automatic ontology construction," , Fortaleza, Ceara, Brazil, 2008, pp. 2248-2255.
@inproceedings{blomqvist_pattern_2008, address = {Fortaleza, Ceara, Brazil},
title = {Pattern ranking for semi-automatic ontology construction},
isbn = {978-1-59593-753-7},
url = {http://portal.acm.org/ft_gateway.cfm?id=1364224&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1363686.1364224},
abstract = {When developing semantic applications, the construction of ontologies is a crucial part. We are developing a semiautomatic ontology construction approach, {OntoCase,} relying on ontology patterns as additional resources. A crucial part of this approach is how to select the appropriate patterns based on the input representation extracted from a text corpus. In this paper, we suggest a pattern ranking and selection approach with the ability to partially bridge the gap between abstract patterns and specific terms, as well as being specifically tuned to the characteristics of ontology patterns. Compared to existing ontology ranking schemes our approach adds indirect matching of terms as well as relation matching. An initial experiment indicates that {OntoCase} ranking performs better, especially when ranking small and abstract patterns, than existing ranking approaches.},
publisher = {{ACM}},
author = {Eva Blomqvist},
year = {2008},
keywords = {Ontologie},
pages = {2248--2255},
annote = {{{\textless}p{\textgreater}blomqvistEva2008.pdf{\textless}/p{\textgreater}}} },
-
D. Oberle, A. Ankolekar, P. Hitzler, P. Cimiano, M. Sintek, M. Kiesel, B. Mougouie, S. Vembu, S. Baumann, and M. Romanelli, "DOLCE ergo SUMO : on foundational and domain models in SWIntO (SmartWeb Integrated Ontology)," Journal of Web Semantics, vol. 3, p. 2007, 2007.
@article{oberle_dolce_2007, title = {{DOLCE} ergo {SUMO} : on foundational and domain models in {SWIntO} {(SmartWeb} Integrated Ontology)},
volume = {3},
shorttitle = {{DOLCE} ergo {SUMO}},
journal = {Journal of Web Semantics},
author = {D. Oberle and A. Ankolekar and P. Hitzler and P. Cimiano and M. Sintek and M. Kiesel and B. Mougouie and S. Vembu and S. Baumann and M. Romanelli},
year = {2007},
keywords = {Ontologie},
pages = {2007} },
-
M. Ruiz-Casado, E. Alfonseca, and P. Castells, "Automatising the learning of lexical patterns : an application to the enrichment of WordNet by extracting semantic relationships from Wikipedia," Data \& Knowledge Engineering, vol. 61, iss. 3, pp. 484-499, 2007.
@article{ruiz-casado_automatisinglearning_2007, title = {Automatising the learning of lexical patterns : an application to the enrichment of {WordNet} by extracting semantic relationships from Wikipedia},
volume = {61},
url = {http://www.sciencedirect.com/science/article/B6TYX-4KF1FYV-5/2/5d9f1244b049a53f3ccb6b0a762e355e},
abstract = {This paper describes an automatic approach to identify lexical patterns that represent semantic relationships between concepts in an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and {WordNet} 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 2600 new relationships that did not appear in {WordNet} originally. The precision of these relationships depends on the degree of generality chosen for the patterns and the type of relation, being around 60-70\% for the best combinations proposed.},
number = {3},
journal = {Data \& Knowledge Engineering},
author = {Maria {Ruiz-Casado} and Enrique Alfonseca and Pablo Castells},
year = {2007},
keywords = {Extraction d'information, Ontologie},
pages = {484--499} },
-
M. Labsky, M. Nekvasil, and V. Svatek, "Towards web information extraction using extraction ontologies and (indirectly) domain ontologies," , Whistler, BC, Canada, 2007, pp. 201-202.
@inproceedings{labsky_towards_2007, address = {Whistler, {BC,} Canada},
title = {Towards web information extraction using extraction ontologies and (indirectly) domain ontologies},
isbn = {978-1-59593-643-1},
url = {http://portal.acm.org/citation.cfm?id=1298406.1298454&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1298406.1298454},
abstract = {Extraction ontologies allow to swiftly proceed from initial domain modelling to running a functional prototype of a web information extraction application. We investigate the possibility of semi-automatically deriving extraction ontologies from third-party domain ontologies.},
publisher = {{ACM}},
author = {Martin Labsky and Marek Nekvasil and Vojtech Svatek},
year = {2007},
keywords = {Extraction d'information, Ontologie, Web},
pages = {201--202},
annote = {{{\textless}p{\textgreater}labskyMartin2007.pdf{\textless}/p{\textgreater}}} },
-
Y. Rezgui, "Text-based domain ontology building using Tf-Idf and metric clusters techniques," The Knowledge Engineering Review, vol. 22, iss. 04, pp. 379-403, 2007.
@article{rezgui_text-based_2007, title = {Text-based domain ontology building using {Tf-Idf} and metric clusters techniques},
volume = {22},
number = {04},
journal = {The Knowledge Engineering Review},
author = {Y. Rezgui},
year = {2007},
keywords = {Cluster, Ontologie},
pages = {379--403} },
-
D. Gaševic, D. Djuric, and V. Devedžic, "MDA-based automatic OWL ontology development," International Journal on Software Tools for Technology Transfer (STTT), vol. 9, iss. 2, pp. 103-117, 2007.
@article{gaevic_mda-based_2007, title = {{MDA-based} automatic {OWL} ontology development},
volume = {9},
number = {2},
journal = {International Journal on Software Tools for Technology Transfer {(STTT)}},
author = {D. Gaševic and D. Djuric and V. Devedžic},
year = {2007},
keywords = {Ontologie},
pages = {103--117} },
-
W. Drabent and A. Wilk, "Extending XML query language Xcerpt by ontology queries," Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 447-451, 2007.
@article{drabent_extending_2007, title = {Extending {XML} query language Xcerpt by ontology queries},
journal = {Proceedings of the {IEEE/WIC/ACM} International Conference on Web Intelligence},
author = {W. Drabent and A. Wilk},
year = {2007},
keywords = {Ontologie},
pages = {447--451} },
-
M. Abulaish and L. Dey, "Biological relation extraction and query answering from MEDLINE abstracts using ontology-based text mining," Data and Knowledge Engineering, vol. 61, iss. 2, pp. 228-262, 2007.
@article{abulaish_biological_2007, title = {Biological relation extraction and query answering from {MEDLINE} abstracts using ontology-based text mining},
volume = {61},
abstract = {The rapid growth of the biological text data repository makes it difficult for human beings to access required information in a convenient and effective manner. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured text that computers cannot interpret very easily. In this paper we have presented an ontology-based Biological Information Extraction and Query Answering {(BIEQA)} System, which initiates text mining with a set of concepts stored in a biological ontology, and thereafter mines possible biological relations among those concepts using {NLP} techniques and co-occurrence-based analysis. The system extracts all frequently occurring biological relations among a pair of biological concepts through text mining. A mined relation is associated to a fuzzy membership value, which is proportional to its frequency of occurrence in the corpus and is termed a fuzzy biological relation. The fuzzy biological relations extracted from a text corpus along with other relevant information components like biological entities occurring within a relation, are stored in a database. The database is integrated with a query-processing module. The query-processing module has an interface, which guides users to formulate biological queries at different levels of specificity. © 2006 Elsevier {B.V.} All rights reserved.},
number = {2},
journal = {Data and Knowledge Engineering},
author = {Muhammad Abulaish and Lipika Dey},
year = {2007},
keywords = {Fouille de donnée, Ontologie},
pages = {228--262},
annote = {{{\textless}p{\textgreater}Compilation} and indexing terms, Copyright 2007 Elsevier Inc. All rights reserved 071210496835 {0169-023X} Biological Information Extraction and Query Answering {(BIEQA)} Systems Text mining Biological relation extraction Biological query processing{\textless}/p{\textgreater}} },
-
G. Jiang, H. Sato, A. Endoh, K. Ogasawara, and T. Sakurai, "An ontological approach to support the description of nursing practice in Japan with the ICNP," International Journal of Medical Informatics, vol. 76, iss. 1, pp. 55-65, 2007.
@article{jiang_ontological_2007, title = {An ontological approach to support the description of nursing practice in Japan with the {ICNP}},
volume = {76},
url = {http://www.sciencedirect.com/science/article/B6T7S-4JKHN7G-1/2/56e5f73f6f0c1e2483db6e7f9c53f63b},
abstract = {Background With increasing computerization of nursing records in Japan, standardization of nursing terminology is becoming imperative. Although some efforts have been made to formalize description of nursing practice in Japan with the International Classification of Nursing Practice {(ICNP),} lack of effective description tools has impacted negatively on the {initiatives.Purpose} To develop and evaluate an ontological approach that could be used to facilitate the description of nursing practice in Japan with the {ICNP.Methodology} An ontology-based support system was developed using Protege-2000, mainly by the following three steps: (1) representing a standard classification of nursing practice (the Nursing Master) in Japan; (2) representing a Japanese version of the {ICNP;} (3) designing an ontology-based framework. A heuristic matching algorithm was developed to automatically match the action labels in the Nursing Master with the terms of the eight axes of the {ICNP} Nursing Actions Classification. A preliminary evaluation was performed to examine the usefulness of the {system.Results} High hit rate was shown on the {ICNP} axes Action Type, Target, and Location. The evaluation indicated that 51.7 +/- 5.8\% (mean +/- {S.D.)} of the action labels with only one action type were properly matched, and that in 80 +/- 4\% (mean +/- {S.D.)} of action labels with more than one action type, at least one valid action type was matched {correctly.Conclusion} The ontology-based approach using a frame-based knowledge representation system (e.g., Protege-2000) is useful for supporting the formal description of nursing practice in Japan with the {ICNP.}},
number = {1},
journal = {International Journal of Medical Informatics},
author = {Guoqian Jiang and Hitomi Sato and Akira Endoh and Katsuhiko Ogasawara and Tsunetaro Sakurai},
year = {2007},
keywords = {Ontologie},
pages = {55--65} },
-
Y. Wu, H. Siy, M. Zand, and V. Winter, "Construction of ontology-based software repositories by text mining." Springer, 2007, pp. 790-797.
@incollection{wu_construction_2007, series = {Lecture notes in computer science; 4489},
title = {Construction of ontology-based software repositories by text mining},
url = {http://dx.doi.org/10.1007/978-3-540-72588-6_128},
abstract = {Software document repositories store artifacts produced in the course of developing software products. But most repositories are simply archives of documents. It is not unusual to find projects where different software artifacts are scattered in unrelated repositories with varying levels of granularity and without a centralized management system. This makes the information available in existing repositories difficult to reuse. In this paper, a methodology for constructing an ontology-based repository of reusable knowledge is presented. The information in the repository is extracted from specification documents using text mining. Ontologies are used to guide the extraction process and organize the extracted information. The methodology is being used to develop a repository of recurring and crosscutting aspects in software specification documents.},
booktitle = {Computational science – {ICCS} 2007 : 7th international conference, Beijing, China, may 27 - 30, 2007 : proceedings, part {III}},
publisher = {Springer},
author = {Yan Wu and Harvey Siy and Mansour Zand and Victor Winter},
year = {2007},
keywords = {Fouille de texte, Ontologie},
pages = {790--797} },
-
RM, "Decisions in thesaurus construction and use," Information Processing \& Management, vol. 43, iss. 4, pp. 958-968, 2007.
@article{losee_decisions_2007, title = {Decisions in thesaurus construction and use},
volume = {43},
issn = {0306-4573},
abstract = {A thesaurus and an ontology provide a set of structured terms, phrases, and metadata, often in a hierarchical arrangement, that may be used to index, search, and mine documents. We describe the decisions that should be made when including a term, deciding whether a term should be subdivided into its subclasses, or determining which of more than one set of possible subclasses should be used. Based on retrospective measurements or estimates of future performance when using thesaurus terms in document ordering, decisions are made so as to maximize performance. These decisions may be used in the automatic construction of a thesaurus. The evaluation of an existing thesaurus is described, consistent with the decision criteria developed here. These kinds of user-focused decision-theoretic techniques may be applied to other hierarchical applications, such as faceted classification systems used in information architecture or the use of hierarchical terms in "breadcrumb navigation". (c) 2006 Elsevier Ltd. All rights reserved.},
number = {4},
journal = {Information Processing \& Management},
author = {{RM} Losee},
month = jul, year = {2007},
keywords = {Ontologie, Thésaurus},
pages = {958--968} },
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M. N. Ahmad and R. M. Colomb, "Managing ontologies: a comparative study of ontology servers," Proceedings of the eighteenth conference on Australasian database-Volume 63, pp. 13-22, 2007.
@article{ahmad_managing_2007, title = {Managing ontologies: a comparative study of ontology servers},
shorttitle = {Managing ontologies},
journal = {Proceedings of the eighteenth conference on Australasian {database-Volume} 63},
author = {M. N. Ahmad and R. M. Colomb},
year = {2007},
keywords = {Ontologie},
pages = {13--22} },
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R. Witte, Q. Li, Y. Zhang, and J. Rilling, "Ontological text mining of software documents," in Natural language processing and information systems : 12th international conference on applications of natural language to information systems, NLDB 2007, Paris, France, june 27-29, 2007 : proceedings, Paris, France, 2007, pp. 168-180.
@inproceedings{witte_ontological_2007, address = {Paris, France},
series = {Lecture notes in computer science; 4592},
title = {Ontological text mining of software documents},
url = {http://dx.doi.org/10.1007/978-3-540-73351-5_15},
abstract = {Documents written in natural languages constitute a major part of the software engineering lifecycle artifacts. Especially during software maintenance or reverse engineering, semantic information conveyed in these documents can provide important knowledge for the software engineer. In this paper, we present a text mining system capable of populating a software ontology with information detected in documents.},
booktitle = {Natural language processing and information systems : 12th international conference on applications of natural language to information systems, {NLDB} 2007, Paris, France, june 27-29, 2007 : proceedings},
publisher = {Springer},
author = {René Witte and Qiangqiang Li and Yonggang Zhang and Juergen Rilling},
year = {2007},
keywords = {Ontologie},
pages = {168--180} },
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Chang-Shing, Yuan-Fang, Yau-Hwang, and Mei-Hui, "Automated ontology construction for unstructured text documents," Data \& Knowledge Engineering, vol. 60, iss. 3, pp. 547-566, 2007.
@article{lee_automated_2007, title = {Automated ontology construction for unstructured text documents},
volume = {60},
issn = {{0169-023X}},
abstract = {Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents. (c) 2006 Elsevier {B.V.} All rights reserved.},
number = {3},
journal = {Data \& Knowledge Engineering},
author = {{Chang-Shing} Lee and {Yuan-Fang} Kao and {Yau-Hwang} Kuo and {Mei-Hui} Wang},
year = {2007},
keywords = {Ontologie},
pages = {547--566} },
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K. Borner, E. Hardy, B. Herr, T. Holloway, and B. W. Paley, "Taxonomy visualization in support of the semi-automatic validation and optimization of organizational schemas," Journal of Informetrics, vol. 1, iss. 3, pp. 214-225, 2007.
@article{borner_taxonomy_2007, title = {Taxonomy visualization in support of the semi-automatic validation and optimization of organizational schemas},
volume = {1},
url = {http://www.sciencedirect.com/science/article/B83WV-4NNPHCR-1/2/5dd27a77a9da3ea4f3ee56dcf7a64f3d},
abstract = {Never before in history has mankind produced and had access to so much data, information, knowledge, and expertise as today. To organize, access, and manage these valuable assets effectively, we use taxonomies, classification hierarchies, ontologies, controlled vocabularies, and other approaches. We create directory structures for our files. We use organizational hierarchies to structure our work environment. However, the design and continuous update of these organizational schemas with potentially thousands of class nodes organizing millions of entities is challenging for any human being. The taxonomy visualization and validation {(TV)} tool introduced in this paper supports the semi-automatic validation and optimization of organizational schemas such as file directories, classification hierarchies, taxonomies, or other structures imposed on a data set for organization, access, and naming. By showing the "goodness of fit" for a schema and the potentially millions of entities it organizes, the {TV} tool eases the identification and reclassification of misclassified information entities, the identification of classes that grow too large, the evaluation of the size and homogeneity of existing classes, the examination of the "well-formedness" of an organizational schema, and more. As a demonstration, the {TV} tool is applied to display and examine the United States Patent and Trademark Office patent classification, which organizes more than three million patents into about 160,000 distinct patent classes. The paper concludes with a discussion and an outlook to future work.},
number = {3},
journal = {Journal of Informetrics},
author = {Katy Borner and Elisha Hardy and Bruce Herr and Todd Holloway and W. Bradford Paley},
year = {2007},
keywords = {Classification, Ontologie, Taxonomie},
pages = {214--225} },
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O. Vasilecas and D. Bugaite, "An algorithm for the automatic transformation of ontology axioms into a rule model," , Bulgaria, 2007, pp. 1-6.
@inproceedings{vasilecas_algorithm_2007, address = {Bulgaria},
title = {An algorithm for the automatic transformation of ontology axioms into a rule model},
isbn = {978-954-9641-50-9},
url = {http://portal.acm.org/ft_gateway.cfm?id=1330610&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1330598.1330610},
abstract = {Quite a number of authors are analysing transformation of ontology into conceptual data model and its usefulness in information systems development, since the semantic content expressed by ontology can be transformed into information systems artefacts, thereby reducing the costs of conceptual modelling. Conceptual data model and ontology have some common aspects, i.e., both include concepts, relationships between them and rules (in ontology -- axioms). However, in the ontology-based conceptual modelling a rule model, which is an important and integral part of each conceptual data model, is often neglected. In this paper the authors proposes the algorithm for the automatic transformation of ontology axioms into a rule model.},
publisher = {{ACM}},
author = {Olegas Vasilecas and Diana Bugaite},
year = {2007},
keywords = {Ontologie},
pages = {1--6},
annote = {{{\textless}p{\textgreater}vasilecasOlegas2007.pdf{\textless}/p{\textgreater}}} },
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J. Trinkunas and O. Vasilecas, "Building ontologies from relational databases using reverse engineering methods," , Bulgaria, 2007, pp. 1-6.
@inproceedings{trinkunas_building_2007, address = {Bulgaria},
title = {Building ontologies from relational databases using reverse engineering methods},
isbn = {978-954-9641-50-9},
url = {http://portal.acm.org/citation.cfm?id=1330598.1330614&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1330598.1330614},
abstract = {The fast changing requirements are the main problem of creating and/or modifying conceptual data models. Most conceptual data models of information systems are created from scratch, wasting time and resources. Ontology represents the real-world domain knowledge. So ontology can be reused in conceptual model building. However ontology engineering is not mature enough. In this paper we propose the new approach to develop ontologies from relational databases using reverse engineering. The ontology can be evaluated, extended and reused as domain knowledge for other conceptual data models.},
publisher = {{ACM}},
author = {Justas Trinkunas and Olegas Vasilecas},
year = {2007},
keywords = {Ontologie},
pages = {1--6},
annote = {{{\textless}p{\textgreater}trinkunasJustas2007.pdf{\textless}/p{\textgreater}}} },
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Y. Li and K. Bontcheva, "Hierarchical, perceptron-like learning for ontology-based information extraction," , Banff, Alberta, Canada, 2007, pp. 777-786.
@inproceedings{li_hierarchical_2007, address = {Banff, Alberta, Canada},
title = {Hierarchical, perceptron-like learning for ontology-based information extraction},
isbn = {978-1-59593-654-7},
url = {http://portal.acm.org/ft_gateway.cfm?id=1242677&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1242572.1242677},
abstract = {Recent work on ontology-based Information Extraction {(IE)} has tried to make use of knowledge from the target ontology in order to improve semantic annotation results. However, very few approaches exploit the ontology structure itself, and those that do so, have some limitations. This paper introduces a hierarchical learning approach for {IE,} which uses the target ontology as an essential part of the extraction process, by taking into account the relations between concepts. The approach is evaluated on the largest available semantically annotated corpus. The results demonstrate clearly the benefits of using knowledge from the ontology as input to the information extraction process. We also demonstrate the advantages of our approach over other state-of-the-art learning systems on a commonly used benchmark dataset.},
publisher = {{ACM}},
author = {Yaoyong Li and Kalina Bontcheva},
year = {2007},
keywords = {Extraction d'information, Ontologie},
pages = {777--786},
annote = {{{\textless}p{\textgreater}liYaoyong2007.pdf{\textless}/p{\textgreater}}} },
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X. Feng and J. Song, "A study in knowledge ontology building in the area of knowledge engineering," Semantics, Knowledge and Grid, Third International Conference on, pp. 586-587, 2007.
@article{feng_study_2007, title = {A study in knowledge ontology building in the area of knowledge engineering},
journal = {Semantics, Knowledge and Grid, Third International Conference on},
author = {X. Feng and J. Song},
year = {2007},
keywords = {Ontologie},
pages = {586--587} },
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P. Doran, V. Tamma, and L. Iannone, "Ontology module extraction for ontology reuse : an ontology engineering perspective," , Lisbon, Portugal, 2007, pp. 61-70.
@inproceedings{doran_ontology_2007, address = {Lisbon, Portugal},
title = {Ontology module extraction for ontology reuse : an ontology engineering perspective},
isbn = {978-1-59593-803-9},
shorttitle = {Ontology module extraction for ontology reuse},
url = {http://portal.acm.org/ft_gateway.cfm?id=1321451&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1321440.1321451},
abstract = {Problems resulting from the management of shared, distributed knowledge has led to ontologies being employed as a solution, in order to effectively integrate information across applications. This is dependent on having ways to share and reuse existing ontologies; with the increased availability of ontologies on the web, some of which include thousands of concepts, novel and more efficient methods for reuse are being devised. One possible way to achieve efficient ontology reuse is through the process of ontology module extraction. A novel approach to ontology module extraction is presented that aims to achieve more efficient reuse of very large ontologies; the motivation is drawn from an Ontology Engineering perspective. This paper provides a definition of ontology modules from the reuse perspective and an approach to module extraction based on such a definition. An abstract graph model for module extraction has been defined, along with a module extraction algorithm. The novel contribution of this paper is a module extraction algorithm that is independent of the language in which the ontology is expressed. This has been implemented in {ModTool;} a tool that produces ontology modules via extraction. Experiments were conducted to compare {ModTool} to other modularisation methods.},
publisher = {{ACM}},
author = {Paul Doran and Valentina Tamma and Luigi Iannone},
year = {2007},
keywords = {Ontologie},
pages = {61--70},
annote = {{{\textless}p{\textgreater}doranPaul2007.pdf{\textless}/p{\textgreater}}} },
-
B. Medjahed and Y. Atif, "Context-based matching for Web service composition," Distributed and Parallel Databases, vol. 21, pp. 5-37, 2007.
@article{medjahed_context-based_2007, title = {Context-based matching for Web service composition},
volume = {21},
url = {http://www.ingentaconnect.com/content/klu/dapd/2007/00000021/00000001/00007003},
abstract = {In this paper, we propose a novel matching framework for Web service composition. The framework combines the concepts of Web service, context, and ontology. We adopt a broad definition of context for Web services, encompassing all information needed for enabling interactions between clients and providers. Context-based matching for Web services requires dealing with three major research thrusts: context categorization, modeling, and matching. We first propose an ontology-based categorization of contextual information in Web service environments. We then define a two-level mechanism for modeling Web service contexts. In the first level, service providers create context specifications using category-specific Web service languages and standards. In the second level, context specifications are enveloped by policies (called context policies) using {WS-Policy} standard. Finally, we present a peer-to-peer architecture for matching context policies. The architecture relies on a context matching engine, context policy assistants, and context community services. Community services implement rule-based techniques for comparing context policies.},
journal = {Distributed and Parallel Databases},
author = {Brahim Medjahed and Yacine Atif},
year = {2007},
keywords = {Ontologie},
pages = {5--37} },
-
A. Saito, K. Umemoto, and M. Ikeda, "A strategy-based ontology of knowledge management technologies," Journal of Knowledge Management, vol. 11, iss. 1, pp. 97-114, 2007.
@article{saito_strategy-based_2007, title = {A strategy-based ontology of knowledge management technologies},
volume = {11},
url = {http://thesius.emeraldinsight.com/10.1108/13673270710728268},
abstract = {Purpose – The purpose of this paper is to distinguish and describe knowledge management {(KM)} technologies according to their support for strategy. Design/methodology/approach – This study employed an ontology development method to describe the relations between technology, {KM} and strategy, and to categorize available {KM} technologies according to those relations. Ontologies are formal specifications of concepts in a domain and their inter-relationships, and can be used to facilitate common understanding and knowledge sharing. The study focused particularly on two sub-domains of the {KM} field: {KM} strategies and {KM} technologies. Findings – {”KM} strategy” has three meanings in the literature: approach to {KM,} knowledge strategy, and {KM} implementation strategy. Also, {KM} technologies support strategy via {KM} initiatives based on particular knowledge strategies and approaches to {KM.} The study distinguishes three types of {KM} technologies: component technologies, {KM} applications, and business applications. They all can be described in terms of ”creation” and ”transfer” knowledge strategies, and ”personalization” and ”codification” approaches to {KM.} Research limitations/implications – The resulting framework suggests that {KM} technologies can be analyzed better in the context of {KM} initiatives, instead of the usual approach associating them with knowledge processes. {KM} initiatives provide the background and contextual elements necessary to explain technology adoption and use. Practical implications – The framework indicates three alternative modes for organizational adoption of {KM} technologies: custom development of {KM} systems from available component technologies; purchase of {KM-specific} applications; or purchase of business-driven applications that embed {KM} functionality. It also lists adequate technologies and provides criteria for selection in any of the cases. Originality/value – Among the many studies analyzing the role of technology in {KM,} an association with strategy has been missing. This paper contributes to filling this gap, integrating diverse contributions via a clearer definition of concepts and a visual representation of their relationships. This use of ontologies as a method, instead of an artifact, is also uncommon in the literature.},
number = {1},
journal = {Journal of Knowledge Management},
author = {Andre Saito and Katsuhiro Umemoto and Mitsuru Ikeda},
year = {2007},
keywords = {Ontologie},
pages = {97 -- 114} },
-
H. J. C. Ellis and G. W. Hislop, "An ontology for software engineering teaching modules," International Journal of Metadata, Semantics and Ontologies, vol. 2, iss. 1, pp. 11-22, 2007.
@article{ellis_ontology_2007, title = {An ontology for software engineering teaching modules},
volume = {2},
number = {1},
journal = {International Journal of Metadata, Semantics and Ontologies},
author = {H. J. C. Ellis and G. W. Hislop},
year = {2007},
keywords = {Informatique, Ontologie},
pages = {11--22} },
-
S. Roberson and D. Dicheva, "Semi-automatic ontology extraction to create draft topic maps," , Winston-Salem, North Carolina, 2007, pp. 100-105.
@inproceedings{roberson_semi-automatic_2007, address = {{Winston-Salem,} North Carolina},
title = {Semi-automatic ontology extraction to create draft topic maps},
url = {http://doi.acm.org/10.1145/1233341.1233360},
abstract = {Topic maps are a Semantic Web technology that provides a human-oriented mechanism to encode knowledge by organizing web information around topics. Studies have shown, however, that authors face major difficulties in constructing topic maps. This paper discusses an approach to automatic construction of a "draft" topic map for the authors to start with. The idea is to extract topic map constructs by crawling a website and parsing its pages. We propose a set of heuristics that can be used for extracting semantic information from the {HTML} markup of the web pages. We have used this approach to design and implement a plug-in for the topic map editor {TM4L} that automatically extracts topics and relationships from a website specified by the author. An evaluation of the proposed approach in terms of Recall and Precision of the extracted data is presented.},
author = {Steven Roberson and Darina Dicheva},
year = {2007},
keywords = {Extraction d'information, Ontologie},
pages = {100 -- 105} },
-
C. Yang, Keng-Chieh, and Hsu-Chieh, "Improving the search process through ontology-based adaptive semantic search," The Electronic Library, vol. 25, iss. 2, pp. 234-248, 2007.
@article{yang_improvingsearch_2007, title = {Improving the search process through ontology-based adaptive semantic search},
volume = {25},
url = {http://thesius.emeraldinsight.com/10.1108/02640470710741359},
abstract = {Purpose – The purpose of this research is to describe an efficient search methodology to help improve the search results in the top portion of a lengthy search list. When facing a lengthy search list, people often limit themselves to the top ten items on the list, even though there may be more useful information after the top ten items. Design/methodology/approach – This study proposes an ontology-based adaptive semantic search to significantly improve the search experience. To capture the semantic difference of search terms, naïve ontology is used to store the relationship among terms. Before a search term is processed by the search engine Lucene, the related words of the search term are selected from ontology structures to form new query phrases in the process of query expansion. The weighting of the expanded query phrases is dynamically learned by observing the users' clicking behaviors. Findings – Research results show that with the aid of ontology the average precision rate of all cases is dramatically higher than the precision rate for the default search result. Even in the worst cases, in some situations, this ontology is still close to the precision rate for the default search result. Originality/value – This paper shows how it is possible to improve the precision rate of items retrieved after a search and thus avoid information overload.},
number = {2},
journal = {The Electronic Library},
author = {Chyan Yang and {Keng-Chieh} Yang and {Hsu-Chieh} Yuan},
year = {2007},
keywords = {Ontologie, Recherche d'information},
pages = {234 -- 248} },
-
O. Romero and A. Abelló, "Automating multidimensional design from ontologies," , Lisbon, Portugal, 2007, pp. 1-8.
@inproceedings{romero_automating_2007, address = {Lisbon, Portugal},
title = {Automating multidimensional design from ontologies},
isbn = {978-1-59593-827-5},
url = {http://portal.acm.org/ft_gateway.cfm?id=1317333&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
doi = {10.1145/1317331.1317333},
abstract = {This paper presents a new approach to automate the multidimensional design of Data Warehouses. In our approach we propose a semi-automatable method aimed to find the business multidimensional concepts from a domain ontology representing different and potentially heterogeneous data sources of our business domain.},
publisher = {{ACM}},
author = {Oscar Romero and Alberto Abelló},
year = {2007},
keywords = {Ontologie},
pages = {1--8} },
-
M. Speretta and S. Gauch, "Automatic ontology identification for reuse." 2007, pp. 419-422.
@inproceedings{speretta_automatic_2007, title = {Automatic ontology identification for reuse},
isbn = {0-7695-3026-5},
url = {http://portal.acm.org/ft_gateway.cfm?id=1331882&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
abstract = {The increasing interest in the Semantic Web is producing a growing number of publicly available domain ontologies. These ontologies are a rich source of information that could be very helpful during the process of engineering other domain ontologies. We present an automatic technique that, given a set of Web documents, selects appropriate domain ontologies from a collection of pre-existing ontologies. We empirically compare an ontology match score that is based on statistical techniques with simple keyword matching algorithms. The algorithms were tested on a set of 183 publicly available ontologies and documents representing ten different domains. Our algorithm was able to select the correct domain ontology as the top ranked ontology 8 out of 10 times.},
publisher = {{IEEE} Computer Society},
author = {Mirco Speretta and Susan Gauch},
year = {2007},
keywords = {Ontologie},
pages = {419--422},
annote = {{{\textless}p{\textgreater}sperettaMirco2007.pdf{\textless}/p{\textgreater}}} },
-
P. Ceravolo, E. Damiani, and M. Viviani, "Bottom-up extraction and trust-based refinement of ontology metadata," Knowledge and Data Engineering, IEEE Transactions on, vol. 19, iss. 2, pp. 149-163, 2007.
@article{paolo_ceravolo_bottom-up_2007, title = {Bottom-up extraction and trust-based refinement of ontology metadata},
volume = {19},
issn = {1041-4347},
doi = {10.1109/TKDE.2006.1599392},
abstract = {We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete {XML} objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run},
number = {2},
journal = {Knowledge and Data Engineering, {IEEE} Transactions on},
author = {Paolo Ceravolo and Ernesto Damiani and Marco Viviani},
year = {2007},
keywords = {Bottom-up, Fuzzy, Ontologie},
pages = {149--163},
annote = {{{\textless}p{\textgreater}ceravoloPaulo2007.pdf{\textless}/p{\textgreater}}} },
-
Sheng-Yuan, Fang-Chen, and Cheng-Seen, "Ontology-supported FAQ processing and ranking techniques," Journal of Intelligent Information Systems, vol. 28, iss. 3, pp. 233-251, 2007.
@article{yang_ontology-supported_2007, title = {Ontology-supported {FAQ} processing and ranking techniques},
volume = {28},
url = {internal-pdf://yang et al., 2007. ontology-supported faq processing and ranking techniques-0001745920/yang et al., 2007. ontology-supported faq processing and ranking techniques.pdf},
abstract = {This paper describes an {FAQ} system on the Personal Computer {(PC)} domain, which employs ontology as the key technique to pre-process {FAQs} and process user query. It is also equipped with an enhanced ranking technique to present retrieved, query-relevant results. Basically, the system bases on the wrapper technique to help clean, retrieve, and transform {FAQ} information collected from a heterogeneous environment and stores it in an ontological database. During retrieval of {FAQs,} the system trims irrelevant query keywords, employs either full keywords match or partial keywords match to retrieve {FAQs,} and removes conflicting {FAQs} before turning the final results to the user. Ontology plays the key role in all the above activities. To produce a more effective presentation of the search results, the system employs an enhanced ranking technique, which includes Appearance Probability, Satisfaction Value, Compatibility Value, and Statistic Similarity Value as four measures properly weighted to rank the {FAQs.} Our experiments show the system does improve precision rate and produces better ranking results. The proposed {FAQ} system manifests the following interesting features. First, the ontology-supported {FAQ} extraction from webpages can clean {FAQ} information by removing redundant data, restore missing data, and resolve inconsistent data. Second, the {FAQs} are stored in an ontology-directed internal format, which supports semantics-constrained retrieval of {FAQs.} Third, the ontology-supported natural language processing of user query helps pinpoint user’s intent. Finally, the partial keywords match-based ranking method helps present user-most-wanted, conflict-free {FAQ} solutions for the user.},
number = {3},
journal = {Journal of Intelligent Information Systems},
author = {{Sheng-Yuan} Yang and {Fang-Chen} Chuang and {Cheng-Seen} Ho},
year = {2007},
keywords = {Ontologie},
pages = {233--251} },
-
B. Bachimont, Ingénierie des connaissances et des contenus : le numérique entre ontologies et documents, Paris: Hermès science publications, 2007.
@book{bachimont_ingnierie_2007, address = {Paris},
series = {Science informatique et {SHS}},
title = {Ingénierie des connaissances et des contenus : le numérique entre ontologies et documents},
isbn = {9782746213692},
publisher = {Hermès science publications},
author = {Bruno Bachimont},
year = {2007},
keywords = {Indexation, Ontologie} },
-
K. Ottens, Marie-Pierre, and P. Glize, "A multi-agent system for building dynamic ontologies," , Honolulu, Hawaii, 2007, pp. 1-7.
@inproceedings{ottens_multi-agent_2007, address = {Honolulu, Hawaii},
title = {A multi-agent system for building dynamic ontologies},
isbn = {978-81-904262-7-5},
url = {http://portal.acm.org/ft_gateway.cfm?id=1329399&type=pdf&coll=ACM&CFID=76094285&CFTOKEN=90417435},
doi = {10.1145/1329125.1329399},
abstract = {Ontologies building from text is still a time-consuming task which justifies the growth of Ontology Learning. Our system named Dynamo is designed along this domain but following an original approach based on an adaptive multi-agent architecture. In this paper we present a distributed hierarchical clustering algorithm, core of our approach. It is evaluated and compared to a more conventional centralized algorithm. We also present how it has been improved using a multi-criteria approach. With those results in mind, we discuss the limits of our system and add as perspectives the modifications required to reach a complete ontology building solution.},
publisher = {{ACM}},
author = {Kévin Ottens and {Marie-Pierre} Gleizes and Pierre Glize},
year = {2007},
keywords = {Ontologie},
pages = {1--7},
annote = {{{\textless}p{\textgreater}ottensKevin2007.pdf{\textless}/p{\textgreater}}} },
-
X. Wang, T. Vitvar, M. Hauswirth, and D. Foxvog, "Building application ontologies from descriptions of semantic Web services." 2007, pp. 337-343.
@inproceedings{wang_building_2007, title = {Building application ontologies from descriptions of semantic Web services},
isbn = {0-7695-3026-5},
url = {http://portal.acm.org/citation.cfm?id=1331740.1331871&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
abstract = {Different ontologies used in semantic web services fields raise numerous interoperation and communication problems with respect to service discovery, composition, and execution. The current approaches for ontology mediation often failed due to their lack of sufficient semantic expressiveness and reasoning capability. In this paper1, we present a novel approach allowing ontologies to provide self-contained semantics for service applications. We show how desired application ontologies can be generated using a new merging algorithm for service ontologies. We also show some experimental results and compare them to the output of the {PROMPT} ontology merging tool.},
publisher = {{IEEE} Computer Society},
author = {Xia Wang and Tomas Vitvar and Manfred Hauswirth and Doug Foxvog},
year = {2007},
keywords = {Ontologie, Web sémantique},
pages = {337--343},
annote = {{{\textless}p{\textgreater}wangXia2007.pdf{\textless}/p{\textgreater}}} },
-
BY and HG, "Web page filtering for domain ontology with the context of concept," IEICE Transactions on information and systems, vol. E90D, iss. 5, pp. 859-862, 2007.
@article{kang_web_2007, title = {Web page filtering for domain ontology with the context of concept},
volume = {{E90D}},
issn = {0916-8532},
url = {http://apps.isiknowledge.com/full_record.do?product=WOS&search_mode=Refine&qid=105&SID=1EOnkFI4kknf@49oNkC&page=1&doc=6},
abstract = {Despite the importance of domain-specific resource construction for domain ontology development, few studies have sought to develop a method for automatically identifying domain ontology-relevant web pages. To address this situation, here we propose a web page filtering scheme for domain ontology that identifies domain-relevant web pages from the web based on the context of concepts. Testing of the proposed filtering scheme with a business domain ontology on {YahooPicks} web pages yielded promising filtering results that were superior to those obtained using the baseline system.},
number = {5},
journal = {{IEICE} Transactions on information and systems},
author = {{BY} Kang and {HG} Kim},
month = may, year = {2007},
keywords = {Ontologie, Web},
pages = {859--862} },
-
T. Lee, "Constraint-based ontology induction from online customer reviews," Group Decision and Negotiation, vol. 16, pp. 255-281, 2007.
@article{lee_constraint-based_2007, title = {Constraint-based ontology induction from online customer reviews},
volume = {16},
url = {http://www.ingentaconnect.com/content/klu/grup/2007/00000016/00000003/00009065},
abstract = {We present an unsupervised, domain-independent technique for inducing a product-specific ontology of product features based upon online customer reviews. We frame ontology induction as a logical assignment problem and solve it with a bounds consistency constrained logic program. Using shallow natural language processing techniques, reviews are parsed into phrase sequences where each phrase refers to a single concept. Traditional document clustering techniques are adapted to collect phrases into initial concepts. We generate a token graph for each initial concept cluster and find a maximal clique to define the corresponding logical set of concept sub-elements. The logic program assigns tokens to clique sub-elements. We apply the technique to several thousand digital camera customer reviews and evaluate the results by comparing them to the ontologies represented by several prominent online buying guides. Because our results are drawn directly from customer comments, differences between our automatically induced product features and those in extant guides may reflect opportunities for better managing customer-producer relationships rather than errors in the process.},
journal = {Group Decision and Negotiation},
author = {Thomas Lee},
year = {2007},
keywords = {Analyse de texte, Fouille de texte, Ontologie},
pages = {255--281} },
-
S. Santini, "Ontology: Use and Abuse," , Paris, 2007.
@inproceedings{santini_ontology:_2007, address = {Paris},
title = {Ontology: Use and Abuse},
url = {http://arantxa.ii.uam.es/~ssantini/writing/papers/s700_keynote.pdf},
author = {Simone Santini},
year = {2007},
keywords = {Ontologie},
annote = {{{\textless}p{\textgreater}simoneSantini2007.pdf{\textless}/p{\textgreater}}} },
-
M. Hepp, "Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies," IEEE Internet Computing, vol. 11, iss. 1, pp. 90-96, 2007.
@article{hepp_possible_2007, title = {Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies},
volume = {11},
issn = {1089-7801},
shorttitle = {Possible Ontologies},
abstract = {Making the Semantic Web a reality demands more and better ontologies. Yet, building ontologies is inherently a social process constrained by technical, social, economic, and legal bottlenecks. That means that researchers must bring the same interest they do to purely technical issues to addressing the other challenges reality imposes on ontology projects.},
number = {1},
journal = {{IEEE} Internet Computing},
author = {Martin Hepp},
year = {2007},
keywords = {Ontologie, Web sémantique},
pages = {90--96} },
-
M. Enkhsaikhan, W. Wong, W. Liu, and M. Reynolds, "Measuring data-driven ontology changes using text mining," , Gold Coast, Australia, 2007, pp. 39-46.
@inproceedings{enkhsaikhan_measuring_2007, address = {Gold Coast, Australia},
title = {Measuring data-driven ontology changes using text mining},
isbn = {978-1-920682-51-4},
url = {http://portal.acm.org/ft_gateway.cfm?id=1378252&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
abstract = {Most current ontology management systems concentrate on detecting usage-driven changes and representing changes formally in order to maintain the consistency. In this paper, we present a semi-automatic approach for measuring and visualising data-driven changes through ontology learning. Terms are first generated using text mining techniques using an ontology learning module, and then classified automatically into clusters. The clusters are then manually named, which is the only manual process in this system. Each cluster is considered as a sub-concept of the root concept, and thus one dimension of the feature space describing the root concept. The changes of terms in each cluster contributes to the change of the root concept. Using our system, Web documents are collected at different time periods and fed into the system to generate different versions of the same ontology for each time period. The paper presents several ways of visualising and analysing the changes. Initial experiments on online media data have demonstrated the promising capabilities of our system.},
publisher = {Australian Computer Society, Inc.},
author = {Majigsuren Enkhsaikhan and Wilson Wong and Wei Liu and Mark Reynolds},
year = {2007},
keywords = {Fouille de texte, Ontologie},
pages = {39--46},
annote = {{{\textless}p{\textgreater}enkhsaikhanMajigsuren2007.pdf{\textless}/p{\textgreater}}} },
-
C. Zhang, J. Du, R. Zhang, X. Fan, Y. Yuan, and T. Ning, "Extracting information of Anti-AIDS inhibitor from the biological literature based on ontology?," Lecture notes in computer science, vol. 4613, p. 74, 2007.
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A. S. Kleshchev and E. A. Shalfeyeva, "Methodology of organizing the catalogue of ontology properties," Automatic Documentation and Mathematical Linguistics, vol. 41, iss. 3, pp. 114-123, 2007.
@article{kleshchev_methodology_2007, title = {Methodology of organizing the catalogue of ontology properties},
volume = {41},
number = {3},
journal = {Automatic Documentation and Mathematical Linguistics},
author = {A. S. Kleshchev and E. A. Shalfeyeva},
year = {2007},
keywords = {Méthodologie, Ontologie},
pages = {114--123} },
-
S. Bloehdorn, A. Hotho, and P. Cimiano, "Learning ontologies to improve text clustering and classification," in From data and information analysis to knowledge engineering : proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Magdeburg, March 9-11, 2005, Berlin; New York, 2006, pp. 334-341.
@inproceedings{bloehdorn_learning_2006, address = {Berlin; New York},
series = {Studies in classification, data analysis, and knowledge organization; 30 (19)},
title = {Learning ontologies to improve text clustering and classification},
isbn = {3540313133 (pbk.) 9783540313137},
url = {www.aifb.uni-karlsruhe.de/WBS/sbl/publications/2006-03-gfkl05-bloehdorn-etal-learning-ontologies.pdf},
doi = {10.1.1.73.6075},
abstract = {Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones.},
booktitle = {From data and information analysis to knowledge engineering : proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation {e.V.,} University of Magdeburg, March 9-11, 2005},
publisher = {Springer},
author = {Stephan Bloehdorn and Andreas Hotho and Philipp Cimiano},
year = {2006},
keywords = {Classification, Ontologie},
pages = {334--341},
annote = {{{\textless}p{\textgreater}bloehdornStephan2005.pdf{\textless}/p{\textgreater}}} },
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J. J. G. Dietz, Enterprise ontology : theory and methodology, Berlin: Springer, 2006.
@book{dietz_enterprise_2006, address = {Berlin},
title = {Enterprise ontology : theory and methodology},
isbn = {9783540291695},
publisher = {Springer},
author = {Jan J. G. Dietz},
year = {2006},
keywords = {Ontologie} },
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A. Mikroyannidis and B. Theodoulidis, "Heraclitus II : a framework for ontology management and evolution." 2006, pp. 514-521.
@inproceedings{mikroyannidis_heraclitus_2006, title = {Heraclitus {II} : a framework for ontology management and evolution},
url = {http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4061423},
abstract = {Ontologies are commonly used as a semantically rich knowledge base in systems that specialize in management of unstructured information. However, the knowledge that ontologies represent is not static, but evolves over time, thus requiring appropriate ontology management and evolution mechanisms. This paper presents Heraclitus {II,} a framework for ontology management and evolution in the context of information management systems. By addressing specific needs of such systems, Heraclitus {II} aims at providing an easily maintained and constantly updated knowledge base.},
author = {Alexander Mikroyannidis and Babis Theodoulidis},
year = {2006},
keywords = {Ontologie},
pages = {514--521} },
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F. Yu, De-Quan, Tie-Jun, S. Li, and H. Yu, "Text classification based on a combination of ontology with statistical method," , Dalian, China, 2006, pp. 1042-1047.
@inproceedings{yu_text_2006, address = {Dalian, China},
series = {Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, {ICMLC} 2006},
title = {Text classification based on a combination of ontology with statistical method},
volume = {2006},
url = {http://dx.doi.org/10.1109/ICMLC.2006.258557 http://dx.doi.org/10.1109/ICMLC.2006.258557},
abstract = {Text classification is becoming one of the key techniques in organizing and handling a large amount of text data. In this paper, a combination of ontology with statistical method is presented to improve the precision of text classification. In this study, first, different kind of linguistic ontology knowledge will be respectively acquired by learning training corpus to determine text classifiers. For an actual document, the semantic evaluation value of the document will respectively be gotten by different kind of linguistic ontology knowledge and the categories will be judged by the highest evaluation value. Compared with Bayes, k-nearest neighbor and support vector machine, the proposed approach outperforms previous works. © 2006 {IEEE.}},
publisher = {Institute of Electrical and Electronics Engineers Computer Society, Piscataway, {NJ} 08855-1331, United States},
author = {Feng Yu and {De-Quan} Zheng and {Tie-Jun} Zhao and Sheng Li and Hao Yu},
year = {2006},
keywords = {Classification, Linguistique, Ontologie},
pages = {1042--1047},
annote = {{{\textless}p{\textgreater}Compilation} and indexing terms, Copyright 2007 Elsevier Inc. All rights reserved 071210502242 Text classification Linguistic ontology knowledge K-nearest neighbor{\textless}/p{\textgreater}} },
-
C. Shirky, L’ontologie est surfaite : catégories, tags et liens, 2006.
@misc{shirky_lontologie_2006, title = {L'ontologie est surfaite : catégories, tags et liens},
url = {http://www.elanceur.org/Articles/OntologieSurfaite.html},
journal = {elanceur},
author = {Clay Shirky},
month = mar, year = {2006},
keywords = {Ontologie},
howpublished = {{http://www.elanceur.org/Articles/OntologieSurfaite.html}} },
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T. Lee, I. Lee, S. Lee, S. Lee, D. Kim, J. Chun, H. Lee, and J. Shim, "Building an operational product ontology system," Electronic Commerce Research and Applications, vol. 5, iss. 1, pp. 16-28, 2006.
@article{lee_buildingoperational_2006, title = {Building an operational product ontology system},
volume = {5},
url = {http://www.sciencedirect.com/science/article/B6X4K-4HKMM9T-1/2/69d3596037e256d742fe6717962d9142},
abstract = {A base of clearly defined product information is a key foundation for an e-commerce system. The manipulation and exchange of semantically enriched and precise product information can enhance the quality of an e-commerce system and offer a high level of interoperability with other systems. Product information consists of product attributes and the relationships between products. Product categorization (or classification) is one type of such relationships. Ontology can play an important role in the formalization of product information. Although the idea of utilizing ontology for {e-Catalogs} has been raised before, we are yet to find an operational implementation of applying ontology in the domain. In this paper, we report on our recent effort to build an operational product ontology system for a government procurement service. The system is designed to serve as a product ontology knowledge base; not only for the design and construction of product databases but also for search and discovery of products. Especially, the keyword-based searching over product ontology database demands different techniques from those over conventional document databases or relational databases, and should be designed to reflect particular characteristics of product ontology. We also introduce some other issues that we have experienced in the project, and those issues include product ontology modeling, ontology construction and maintenance, and visualization. Our work presented herein may serve as a reference model for similar projects in the future.},
number = {1},
journal = {Electronic Commerce Research and Applications},
author = {Taehee Lee and Ig-hoon Lee and Suekyung Lee and Sang-goo Lee and Dongkyu Kim and Jonghoon Chun and Hyunja Lee and Junho Shim},
year = {2006},
keywords = {Ontologie},
pages = {16--28} },
-
P. Buitelaar, P. Cimiano, S. Racioppa, and M. Siegel, "Ontology-based information extraction with SOBA," Proc. of the International Conference on Language Resources and Evaluation (LREC), 2006.
@article{buitelaar_ontology-based_2006, title = {Ontology-based information extraction with {SOBA}},
journal = {Proc. of the International Conference on Language Resources and Evaluation {(LREC)}},
author = {P. Buitelaar and P. Cimiano and S. Racioppa and M. Siegel},
year = {2006},
keywords = {Extraction d'information, Ontologie} },
-
D. Godoy and A. Amandi, "Modeling user interests by conceptual clustering," Information Systems, vol. 31, iss. 4-5, pp. 247-265, 2006.
@article{godoy_modeling_2006, title = {Modeling user interests by conceptual clustering},
volume = {31},
url = {http://www.sciencedirect.com/science/article/B6V0G-4FSCJS5-1/2/048b1311cd7a7e3695597e882369116a},
abstract = {As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents rely on having knowledge about users contained into user profiles, i.e., models of user preferences and interests gathered by observation of user behavior, the capacity of acquiring and modeling user interest categories has become a critical component in personal agent design. User profiles have to summarize categories corresponding to diverse user information interests at different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, document clustering offers the advantage that an a priori knowledge of categories is not needed, therefore the categorization is completely unsupervised. In this paper we present a document clustering algorithm, named {WebDCC} {(Web} Document Conceptual Clustering), that carries out incremental, unsupervised concept learning over Web documents in order to acquire user profiles. Unlike most user profiling approaches, this algorithm offers comprehensible clustering solutions that can be easily interpreted and explored by both users and other agents. By extracting semantics from Web pages, this algorithm also produces intermediate results that can be finally integrated in a machine-understandable format such as an ontology. Empirical results of using this algorithm in the context of an intelligent Web search agent proved it can reach high levels of accuracy in suggesting Web pages.},
number = {4-5},
journal = {Information Systems},
author = {Daniela Godoy and Analia Amandi},
year = {2006},
keywords = {Cluster, Ontologie},
pages = {247--265} },
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J. Park and A. Cheyer, "Just for me : topic maps and ontologies." Springer, 2006, pp. 145-159.
@incollection{park_just_2006, series = {Lecture notes in computer science; 3873. Lecture notes in artificial intelligence},
title = {Just for me : topic maps and ontologies},
isbn = {978-3-540-32527-7},
shorttitle = {Just for me},
url = {http://dx.doi.org/10.1007/11676904_13},
abstract = {The development of the {IRIS} semantic desktop platform has provided illumination of some important issues associated with the collection and manipulation of knowledge assets that are organized by an ontology. We explore those issues related to the personalization of the workspace and of the knowledge assets manipulated by {IRIS} users. We show that a topic map can provide a necessary mediation between the formal organization provided by an ontology to serve the needs of semantic interoperability between workstations and the individual’s need to personalize the workspace in a just for me fashion.},
booktitle = {Charting the topic maps research and applications landscape : first international workshop on topic map research and applications, {TMRA} 2005, Leipzig, Germany, october 6-7, 2005 : revised selected papers},
publisher = {Springer},
author = {Jack Park and Adam Cheyer},
year = {2006},
keywords = {Ontologie},
pages = {145--159},
annote = {{{\textless}p{\textgreater}parkJack2006.pdf{\textless}/p{\textgreater}}} },
-
R. Valencia-Garcia, D. Castellanos-Nieves, J. T. Fernandez-Breis, and P. J. Vivancos-Vicente, "A methodology for extracting ontological knowledge from Spanish documents," in Computational linguistics and intelligent text processing : 7th international conference , CICLing 2006, Mexico City, Mexico, february 19-25, 2006 : proceedings, 2006, pp. 71-80.
@inproceedings{valencia-garcia_methodology_2006, series = {Lecture notes in computer science; 3878},
title = {A methodology for extracting ontological knowledge from Spanish documents},
isbn = {0302-9743 {(Print)} 1611-3349 {(Online)}},
url = {http://www.springerlink.com/content/ql5255376807/},
doi = {10.1007/11671299},
abstract = {This paper presents a semi-automatic approach for extracting knowledge from natural language texts in Spanish. The knowledge is acquired and learned through the combination of {NLP} techniques for analyzing text fragments, the ontological technology for representing knowledge and {MCRDR,} a case based reasoning methodology. This approach has been applied in the oncology domain and the results of this application are discussed in this work},
booktitle = {Computational linguistics and intelligent text processing : 7th international conference , {CICLing} 2006, Mexico City, Mexico, february 19-25, 2006 : proceedings},
publisher = {{Springer-Verlag}},
author = {Rafael {Valencia-Garcia} and Dagoberto {Castellanos-Nieves} and Jesualdo Tomas {Fernandez-Breis} and Pedro José {Vivancos-Vicente}},
year = {2006},
note = {Copyright 2006, The institution of engineering and technology},
keywords = {Ontologie},
pages = {71--80},
annote = {{\textless}p{\textgreater}8856760 ontological knowledge extraction Spanish text document semiautomatic knowledge extraction natural language processing text fragment analysis ontological technology knowledge representation multiple classification ripple down rules case based reasoning{\textless}/p{\textgreater}},
annote = {{{\textless}p{\textgreater}valencia-garciaRafael2006.pdf{\textless}/p{\textgreater}}} },
-
P. Cimiano, F. Ciravegna, J. Domingue, S. Handschuh, A. Lavelli, S. Staab, and M. Stevenson, "Requirements for information extraction for knowledge management," , 2006.
@article{cimiano_requirements_2006, title = {Requirements for information extraction for knowledge management},
url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-101/Philipp_Cimiano-et-al.pdf},
abstract = {Knowledge Management {(KM)} systems inherently suffer from the knowledge acquisition bottleneck - the difficulty of modeling and formalizing knowledge relevant for specific domains. A potential solution to this problem is Information Extraction {(IE)} technology. However, {IE} was originally developed for database population and there is a mismatch between what is required to successfully perform {KM} and what current {IE} technology provides. In this paper we begin to address this issue by outlining requirements for {IE} based {KM.}},
author = {Philipp Cimiano and Fabio Ciravegna and John Domingue and Siegfried Handschuh and Alberto Lavelli and S. Staab and Mark Stevenson},
year = {2006},
keywords = {Extraction d'information, Gestion des connaissances, Ontologie},
annote = {{{\textless}p{\textgreater}cimianoPhilipp2006.pdf{\textless}/p{\textgreater}}} },
-
R. Klischewski, "Ontologies for e-document management in public administration," Business Process Management Journal, vol. 12, iss. 1, pp. 34-47, 2006.
@article{klischewski_ontologies_2006, title = {Ontologies for e-document management in public administration},
volume = {12},
url = {http://thesius.emeraldinsight.com/10.1108/14637150610643742},
abstract = {Purpose – This research seeks to explore the potential of ontologies for reorganizing e-document management in public administration with the aim of supporting administration in organizing cross-organizational document and information management. Design/methodology/approach – Since ontologies are suitable for organizing metadata for annotation of informational resources, the research question is: How can public administrations make use of ontologies for organizing and improving their e-document management? Findings, based on an action research project in the state administration of {Schleswig-Holstein} {(Germany).} Findings – The research findings indicate that structuring documents and information through ontologies requires a socio-technical infrastructure consisting of a number of regularities, services and support on the level of organization as well as information technology. Research limitations/implications – Since the case of {Schleswig-Holstein} is typical for governments trying to enter the information age without having the power and resources to be on the leading edge, the recommendations based on this research may support the strategy development and solution finding in other administrations as well. Practical implications – A rather small government (such as that of {Schleswig-Holstein)} must be aware of its strategic goals and step ahead carefully in order to avoid the risks of misinvestment while reorganizing its e-document management. Originality/value – The paper systematically addresses the question {“How} can public administration make use of ontologies for organizing and improving their e-document management?”.},
number = {1},
journal = {Business Process Management Journal},
author = {Ralf Klischewski},
year = {2006},
keywords = {Ontologie},
pages = {34 -- 47} },
-
F. Bellomi and M. Cristani, "Supervised document classification based upon domain-specific term taxonomies," International Journal of Metadata, Semantics and Ontologies, vol. 1, iss. 1, pp. 37-46, 2006.
@article{bellomi_supervised_2006, title = {Supervised document classification based upon domain-specific term taxonomies},
volume = {1},
issn = {1744-2621},
url = {http://www.ingentaconnect.com/content/ind/ijmso/2006/00000001/00000001/art00003M3 - "doi:10.1504/IJMSO.2006.008768" http://dx.doi.org/10.1504/IJMSO.2006.008768},
abstract = {The classification of documents is an interesting topic of recent terminological investigations, in particular the technological ones. Some sophisticated techniques have been developed which provide the classification based upon the recognition of specific linguistic features, such as specific terms or occurrences of phrases. A limited number of cases exist of real document classification applications that make use of natural language processing techniques providing both statistical analysis and human supervision, where the system fully automates the classification process, but the instruction of the taxonomy is a totally human centred activity. In this paper we focus on an application with the above mentioned features; we then introduce a methodology that makes use of this application. The fundamental argument in favour of a specific methodology is that the analysis which leads to the deployment of the term 'taxonomy' can be seen as an ontology construction: we also discuss this aspect as a general motivation.},
number = {1},
journal = {International Journal of Metadata, Semantics and Ontologies},
author = {Francesco Bellomi and Matteo Cristani},
year = {2006},
keywords = {Catégorisation, Classification, Cluster, Ontologie, Taxonomie},
pages = {37--46} },
-
P. Benjamin, M. Patki, and R. Mayer, "Using ontologies for simulation modeling," , Monterey, California, 2006, pp. 1151-1159.
@inproceedings{benjamin_using_2006, address = {Monterey, California},
title = {Using ontologies for simulation modeling},
url = {http://portal.acm.org/citation.cfm?id=1218321&coll=portal&dl=ACM&CFID=26729002&CFTOKEN=72789466},
abstract = {Ontological analysis has been shown to be an effective first step in the construction of robust knowledge based systems. However, the modeling and simulation community has not taken advantage of the benefits of ontology management methods and tools. Moreover, the popularity of semantic technologies and the semantic web has provided several beneficial opportunities for the modeling and simulation communities of interest. This paper describes the role of ontologies in facilitating simulation modeling. It outlines the technical challenges in distributed simulation modeling and describes how ontology-based methods may be applied to address these challenges. The paper concludes by describing an ontology-based solution framework for simulation modeling and analysis and outlining the benefits of this solution approach.},
author = {Perakath Benjamin and Mukul Patki and Richard Mayer},
year = {2006},
keywords = {Ontologie},
pages = {1151 -- 1159} },
-
Z. Dequan, Z. Tiejun, L. Sheng, and Y. Hao, "Linguistic knowledge representation and automatic acquisition based on a combination of ontology with statistical method," in Knowledge science, engineering and management : first international conference, KSEM 2006, Guilin, China, august 5-8, 2006 : proceedings, 2006, pp. 637-649.
@inproceedings{dequan_linguistic_2006, series = {Lecture notes in computer science; 4092},
title = {Linguistic knowledge representation and automatic acquisition based on a combination of ontology with statistical method},
abstract = {Due to the complexity and flexibility of natural language, linguistic knowledge representation, automatic acquisition and its application research becomes difficult. In this paper, a combination of ontology with statistical method is presented for linguistic knowledge representation and acquisition from training data. In this study, linguistic knowledge representation is firstly defined using ontology theory, and then, linguistical knowledge is automatically acquired by statistical method. In document processing, the semantic evaluation value of the document can be get by linguistic knowledge. The experimentation in Chinese information retrieval and text classification shows the proposed method improves the precision of natural language processing},
booktitle = {Knowledge science, engineering and management : first international conference, {KSEM} 2006, Guilin, China, august 5-8, 2006 : proceedings},
publisher = {{Springer-Verlag}},
author = {Zheng Dequan and Zhao Tiejun and Li Sheng and Yu Hao},
year = {2006},
note = {Copyright 2006, The Institution of Engineering and Technology},
keywords = {Langage naturel, Ontologie},
pages = {637--649},
annote = {{\textless}p{\textgreater}9055323 linguistic knowledge representation automatic knowledge acquisition ontology statistical method document processing semantic evaluation information retrieval text classification natural language processing{\textless}/p{\textgreater}} },
-
A. Doms, V. Jakonienė, P. Lambrix, M. Schroeder, and T. Wächter, "Ontologies and text mining as a basis for a semantic Web for the life sciences," in Reasoning Web. Second international summer school 2006, Lisbon, Portugal, september 4-8, 2006 : tutorial lectures, Berlin; Heidelberg, 2006, pp. 164-183.
@inproceedings{doms_ontologies_2006, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 4126},
title = {Ontologies and text mining as a basis for a semantic Web for the life sciences},
isbn = {978-3-540-38409-0},
url = {http://dx.doi.org/10.1007/11837787_7},
abstract = {The life sciences are a promising application area for semantic web technologies as there are large online structured and unstructured data repositories and ontologies, which structure this knowledge. We briefly give an overview over biomedical ontologies and show how they can help to locate, retrieve, and integrate biomedical data. Annotating literature with ontology terms is an important problem to support such ontology-based searches. We review the steps involved in this text mining task and introduce the ontology-based search engine {GoPubMed.} As the underlying data sources evolve, so do the ontologies. We give a brief overview over different approaches supporting the semi-automatic evolution of ontologies.},
booktitle = {Reasoning Web. Second international summer school 2006, Lisbon, Portugal, september 4-8, 2006 : tutorial lectures},
publisher = {Springer},
author = {Andreas Doms and Vaida Jakonienė and Patrick Lambrix and Michael Schroeder and Thomas Wächter},
year = {2006},
keywords = {Ontologie},
pages = {164--183} },
-
I. Yoo, X. Hu, and Il-Yeol, "Integrating biomedical literature clustering and summarization approaches using biomedical ontology," , Arlington, Virginia, USA, 2006, pp. 37-42.
@inproceedings{yoo_integrating_2006, address = {Arlington, Virginia, {USA}},
title = {Integrating biomedical literature clustering and summarization approaches using biomedical ontology},
url = {http://doi.acm.org/10.1145/1183535.1183545},
abstract = {We introduce a method that integrates biomedical literature clustering and summarization using biomedical ontology. The core of the approach is to identify document cluster models as semantic chunks capturing the core semantic relationships in the ontology-enriched scale-free graphical representation of documents. These document cluster models are used for both document clustering on document assignment and text summarization on the construction of Text Semantic Interaction Network {(TSIN).} Our experimental results show our approach is superior to traditional approaches including Bisecting K-means as a leading document clustering approach in terms of cluster quality and clustering reliability. In addition, our approach provides concise but rich text summary in key concepts and sentences.},
author = {Illhoi Yoo and Xiaohua Hu and {Il-Yeol} Song},
year = {2006},
keywords = {Catégorisation, Ontologie},
pages = {37 -- 42} },
-
W. Xu, W. Li, M. Wu, W. Li, and C. Yuan, "Deriving event relevance from the ontology constructed with formal concept analysis." Springer, 2006, pp. 480-489.
@incollection{xu_deriving_2006, series = {Lecture notes in computer science; 3878},
title = {Deriving event relevance from the ontology constructed with formal concept analysis},
isbn = {978-3-540-32205-4},
abstract = {In this paper, we present a novel approach to derive event relevance from event ontology constructed with Formal Concept Analysis {(FCA),} a mathematical approach to data analysis and knowledge representation. The ontology is built from a set of relevant documents and according to the named entities associated to the events. Various relevance measures are explored, from binary to scaled, and from symmetrical to asymmetrical associations. We then apply the derived event relevance to the task of multi-document summarization. The experiments on {DUC} 2004 data set show that the relevant-event-based approaches outperform the independent-event-based approach.},
booktitle = {Computational linguistics and intelligent text processing},
publisher = {Springer},
author = {Wei Xu and Wenjie Li and Mingli Wu and Wei Li and Chunfa Yuan},
year = {2006},
keywords = {Ontologie},
pages = {480--489},
annote = {{{\textless}p{\textgreater}xuWei2006.pdf{\textless}/p{\textgreater}}} },
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E. Sachs, Getting started with protege-frames, 2006.
@misc{sachs_getting_2006, title = {Getting started with protege-frames},
url = {http://protege.stanford.edu/doc/tutorial/get_started/get-started.pdf},
author = {Eliza Sachs},
month = jun, year = {2006},
keywords = {Méthodologie, Ontologie},
annote = {{{\textless}p{\textgreater}sachsEliza2006.pdf{\textless}/p{\textgreater}}} },
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C. Caracciolo, "Designing and implementing an ontology for logic and linguistics," Literary and Linguistic Computing, vol. 21, pp. 29-39, 2006.
@article{caracciolo_designing_2006, title = {Designing and implementing an ontology for logic and linguistics},
volume = {21},
issn = {02681145},
abstract = {This article reports on the design and implementation of a domain-specific, manually built ontology, as a scaffolding for the electronic publication of and access to scientific handbooks. The ontology will provide access to the case study handbook (the Handbook of Logic and Language, van Benthem, J. and ter Meulen, A. (1997)) by means of semi-automatically detected links. The ontology's structure is given by a set of hierarchical relations plus two non-hierarchical relations introduced for navigational purposes. The main expected advantage of this way of providing access to the text consists in an enhanced browsing system, providing the user with an explicit map of the contents of the handbook. Moreover, by manually designing and populating the hierarchy, we expect a coherent representation of the domain and a good quality result. When designing and implementing the ontology, we put into practice some of the ideas and technologies already available from the Semantic Web. In the course of this study we describe the design and implementation of the ontology, and draw some preliminary conclusions about the feasibility of such an enterprise and its suitability to support reading in an electronic environment. Here we do not treat the issue of automatically generating links from the ontology to the text, which is currently a work in progress.},
journal = {Literary and Linguistic Computing},
author = {Caterina Caracciolo},
year = {2006},
keywords = {Ontologie},
pages = {29--39},
annote = {{{\textless}p{\textgreater}caraccioloCaterina2006.pdf{\textless}/p{\textgreater}}} },
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N. Aussenac-Gilles and M. P. Jacques, "Designing and evaluating patterns for ontology enrichment from texts," in Managing knowledge in a world of networks : 15th international conference, EKAW 2006, Podebrady, Czech Republic, october 2-6, 2006 : proceedings, Berlin; Heidelberg, 2006, pp. 158-165.
@inproceedings{aussenac-gilles_designing_2006, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 4248. Lecture notes in artificial intelligence},
title = {Designing and evaluating patterns for ontology enrichment from texts},
isbn = {978-3-540-46363-4},
abstract = {Pattern-based approaches for knowledge identification in texts assume that linguistic regularities always characterise the same kind of knowledge, such as semantic relations. We report the experimental evaluation of a large set of patterns using an ontology enrichment tool: {CAMELEON.} Results underline the strong corpus influence on the patterns efficiency and on their meaning. This influence confirms two of the hypotheses that motivated to define {CAMELEON} as a support used in a supervised process: (1) patterns and relations must be adapted to each project; (2) human interpretation is required to decide how to report in the ontology the pieces of knowledge identified with patterns.},
booktitle = {Managing knowledge in a world of networks : 15th international conference, {EKAW} 2006, Podebrady, Czech Republic, october 2-6, 2006 : proceedings},
publisher = {Springer},
author = {N. {Aussenac-Gilles} and M. P. Jacques},
year = {2006},
keywords = {Design, Ontologie},
pages = {158--165} },
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T. Wächter, A. Wobst, M. Schroeder, H. Tan, and P. Lambrix, "A corpus-driven approach for design, evolution and alignment of ontologies," , Monterey, California, 2006, pp. 1595-1602.
@inproceedings{wchter_corpus-driven_2006, address = {Monterey, California},
title = {A corpus-driven approach for design, evolution and alignment of ontologies},
url = {http://portal.acm.org/citation.cfm?id=1218402&coll=portal&dl=ACM&CFID=26729002&CFTOKEN=72789466},
abstract = {Bio-ontologies are hierarchical vocabularies, which are used to annotate other data sources such as sequence and structure databases. With the wide use of ontologies their integration, design, and evolution becomes an important problem. We show how textmining on relevant text corpora can be used to identify matching ontology terms of two separate ontologies and to propose new ontology terms for a given term. We evaluate these approaches on the {GeneOntology.}},
author = {Thomas Wächter and André Wobst and Michael Schroeder and He Tan and Patrick Lambrix},
year = {2006},
keywords = {Design, Ontologie},
pages = {1595 -- 1602} },
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C. V. Fabritius, "Finding the best visualization of an ontology," Journal of the Operational Research Society, vol. 57, iss. 12, pp. 1482-1490, 2006.
@article{fabritius_findingbest_2006, title = {Finding the best visualization of an ontology},
volume = {57},
url = {http://www.ingentaconnect.com/content/pal/01605682/2006/00000057/00000012/art00010},
abstract = {An ontology is a classification model for a given domain. In information retrieval, ontologies are used to perform broad searches. An ontology can be visualized as nodes and edges. Each node represents an element and each edge a relation between a parent and a child element. Working with an ontology becomes easier with a visual representation. An idea is to use the expressive power of {3D} representation to provide visualization for the user. In this paper, we propose a new method for positioning the elements of the visualized concept lattice in the {3D} world based on operations research {(OR)} methods. One method uses a discrete location model to create an initial solution and we propose heuristic methods to further improve the visual result. We evaluate the visual results according to our success criteria and the feedback from users. Running times of the heuristic indicate that an improved version should be feasible for on-line processing and what-if analysis of ontologies.},
number = {12},
journal = {Journal of the Operational Research Society},
author = {C. V. Fabritius},
year = {2006},
keywords = {Classification, Ontologie, Visualisation de l'information},
pages = {1482--1490} },
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M. Morneau, G. W. Mineau, and D. Corbett, "SeseiOnto : interfacing NLP and ontology extraction." 2006, pp. 449-455.
@inproceedings{morneau_seseionto_2006, title = {{SeseiOnto} : interfacing {NLP} and ontology extraction},
url = {http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4061410},
abstract = {For many years, information retrieval tools have been used to try to solve the information overload problem which was accentuated by the coming of age of the World Wide Web. Some tools used Boolean search, others, natural language based processing {(NLP).} Ontology-based techniques were proposed to improve the quality of the search but none were widely adopted since they did not statistically enhance either the recall or the precision of the search. However, when it comes to information extraction, they may be of significant help. Their integration in professional search engines has been rather slow, partially due to the fact that the ontology building process is time consuming. In this paper, we describe the {SeseiOnto} software, which uses simple artificial intelligence techniques to improve information extraction and retrieval. To assist the {NLP-based} information retrieval on a corpus of documents, {SeseiOnto} employs an automatically generated ontology. Under our experiments, we found that {SeseiOnto} obtained results comparable to a traditional search engine, while providing a natural language interface to its user.},
author = {Maxime Morneau and Guy W. Mineau and Dan Corbett},
year = {2006},
keywords = {Ontologie},
pages = {449--455} },
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A. Maguitman, F. Menczer, F. Erdinc, H. Roinestad, and A. Vespignani, "Algorithmic computation and approximation of semantic similarity," World Wide Web, vol. 9, pp. 431-456, 2006.
@article{maguitman_algorithmic_2006, title = {Algorithmic computation and approximation of semantic similarity},
volume = {9},
url = {http://www.ingentaconnect.com/content/klu/wwwj/2006/00000009/00000004/00008562},
abstract = {Automatic extraction of semantic information from text and links in Web pages is key to improving the quality of search results. However, the assessment of automatic semantic measures is limited by the coverage of user studies, which do not scale with the size, heterogeneity, and growth of the Web. Here we propose to leverage human-generated metadata—namely topical directories—to measure semantic relationships among massive numbers of pairs of Web pages or topics. The Open Directory Project classifies millions of {URLs} in a topical ontology, providing a rich source from which semantic relationships between Web pages can be derived. While semantic similarity measures based on taxonomies (trees) are well studied, the design of well-founded similarity measures for objects stored in the nodes of arbitrary ontologies (graphs) is an open problem. This paper defines an information-theoretic measure of semantic similarity that exploits both the hierarchical and non-hierarchical structure of an ontology. An experimental study shows that this measure improves significantly on the traditional taxonomy-based approach. This novel measure allows us to address the general question of how text and link analyses can be combined to derive measures of relevance that are in good agreement with semantic similarity. Surprisingly, the traditional use of text similarity turns out to be ineffective for relevance ranking.},
journal = {World Wide Web},
author = {Ana Maguitman and Filippo Menczer and Fulya Erdinc and Heather Roinestad and Alessandro Vespignani},
year = {2006},
keywords = {Ontologie},
pages = {431--456} },
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K. Wolstencroft, P. Lord, L. Tabernero, A. Brass, and R. Stevens, "Protein classification using ontology classification," Bioinformatics, vol. 22, iss. 14, p. e530-e538, 2006.
@article{wolstencroft_protein_2006, title = {Protein classification using ontology classification},
volume = {22},
url = {http://www.ingentaconnect.com/content/oup/cabios/2006/00000022/00000014/e530},
abstract = {Motivation: The classification of proteins expressed by an organism is an important step in understanding the molecular biology of that organism. Traditionally, this classification has been performed by human experts. Human knowledge can recognise the functional properties that are sufficient to place an individual gene product into a particular protein family group. Automation of this task usually fails to meet the `gold standard' of the human annotator because of the difficult recognition stage. The growing number of genomes, the rapid changes in knowledge and the central role of classification in the annotation process, however, motivates the need to automate this process. Results: We capture human understanding of how to recognise members of the protein phosphatases family by domain architecture as an ontology. By describing protein instances in terms of the domains they contain, it is possible to use description logic reasoners and our ontology to assign those proteins to a protein family class. We have tested our system on classifying the protein phosphatases of the human and Aspergillus fumigatus genomes and found that our knowledge-based, automatic classification matches, and sometimes surpasses, that of the human annotators. We have made the classification process fast and reproducible and, where appropriate knowledge is available, the method can potentially be generalised for use with any protein family. Availability: All components described in this paper are freely available. {OWL} ontology http://www.bioinf.man.ac.uk/phosphabase {myGrid} http://www.mygrid.org.uk Instance Store http://instancestore.man.ac.uk},
number = {14},
journal = {Bioinformatics},
author = {K. Wolstencroft and P. Lord and L. Tabernero and A. Brass and R. Stevens},
year = {2006},
keywords = {Classification, Ontologie},
pages = {e530--e538} },
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L. Dey, A. C. Rastogi, and S. Kumar, "Generating concept ontologies through text mining." 2006, pp. 23-32.
@inproceedings{dey_generating_2006, title = {Generating concept ontologies through text mining},
isbn = {0-7695-2747-7},
url = {http://portal.acm.org/ft_gateway.cfm?id=1249038&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435},
abstract = {Designing mechanisms for creating concept ontologies automatically is an important research problem. In this work we have proposed a rough-set based mechanism to generate concept ontologies with concepts mined from documents. When the concept ontology is mined from preclassified documents, the output signifies the core set of domain concepts and their inter-relationships that define the categories, as well as the inter-category relationships. When the ontology is mined from a heterogeneous collection, the documents are first clustered into homogeneous groups and then mined for concepts. Rough set based lower and upper approximations have been used to identify core concepts and associated concepts for a domain or a group. The scheme has been tested over multiple domains.},
publisher = {{IEEE} Computer Society},
author = {Lipika Dey and Ashish Chandra Rastogi and Sachin Kumar},
year = {2006},
keywords = {Fouille de texte, Ontologie},
pages = {23--32},
annote = {{{\textless}p{\textgreater}deyLipika2006.pdf{\textless}/p{\textgreater}}} },
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X. Lacot, Introduction à OWL, un langage XML d’ontologie Web, 2006.
@misc{lacot_introduction_2006, type = {{PDF} d'une présentation {PowerPoint}},
title = {Introduction à {OWL,} un langage {XML} d'ontologie Web},
author = {Xavier Lacot},
year = {2006},
keywords = {Ontologie, Web},
annote = {{{\textless}p{\textgreater}lacotXavier2006.pdf{\textless}/p{\textgreater}}} },
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K. X. S. de Souza, J. Davis, and S. R. M. de Evangelista, "Aligning ontologies, evaluating concept similarities and visualizing results." Berlin; Heidelberg: Springer, 2006, pp. 211-236.
@incollection{sampaio_de_souza_aligning_2006, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 3870},
title = {Aligning ontologies, evaluating concept similarities and visualizing results},
abstract = {Ontologies have been created for many different subjects and by independent groups around the world. The nonexistence of a commonly accepted and used general purpose upper-ontology makes it difficult to integrate these ontologies through merge and alignment operations. The majority of the algorithms proposed so far rely on syntactic analysis, disregarding the structural properties of the source ontologies. In our previous work, we proposed an alignment method that considers the structural properties of an upper-ontology constructed using a thesaurus and Formal Concept Analysis technique {(FCA).} We also analyzed the {FCA’s} lattice structure and proposed a measure of similarity based on Tversky’s model, which allowed us to identify closely related concepts in different source ontologies. In this paper, we apply the alignment method to ontologies developed for a completely different domain, and enhance the solution by providing a navigational aid for the lattice. It is well known that one of the main drawbacks of the application of {FCA} is that the resulting lattice soon becomes cluttered when the number of objects and attributes increases. The proposed solution is based on hyperbolic visualization and on structural elements of the lattice. Keywords: ontology alignment, Formal Concept Analysis, lattice visualization, similarity measures},
booktitle = {Journal on data semantics V},
publisher = {Springer},
author = {Kleber Xavier Sampaio de Souza and Joseph Davis and Silvio Roberto de Medeiros Evangelista},
year = {2006},
keywords = {Ontologie, Visualisation de l'information},
pages = {211--236},
annote = {{{\textless}p{\textgreater}sampaioKleber2006.pdf{\textless}/p{\textgreater}}} },
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M. Grobelnik, J. Brank, D. Mladenic, B. Novak, and B. Fortuna, "Using DMoz for constructing ontology from data stream," , Cavtat/Dubrovnik, Croatia, 2006, p. 6.
@inproceedings{grobelnik_using_2006, address = {{Cavtat/Dubrovnik,} Croatia},
series = {{ITI} 2006. Proceedings of the 28th International Conference on Information Technology Interfaces},
title = {Using {DMoz} for constructing ontology from data stream},
abstract = {This paper presents an approach for constructing an ontology from a stream of documents. Named entities extracted from the documents are used as instances of the ontology. Entities and co-occurring entity pairs are represented by feature vectors based on the content of the documents where they occurred. In general, concepts and relations can be formed into an ontological structure either by clustering or by classification into an existing topic hierarchy. We propose the latter using {DMoz} as an existing topic hierarchy. The approach is efficient and can scale to large data sets. We propose a framework that incorporates the stream mining process into a formal definition of the ontology. We describe a software component implementing this approach, and present experiments using a large collection of news},
publisher = {{IEEE}},
author = {M. Grobelnik and J. Brank and D. Mladenic and B. Novak and B. Fortuna},
year = {2006},
note = {Copyright 2006, The Institution of Engineering and Technology},
keywords = {Fouille de donnée, Ontologie, Recherche d'information, Web},
pages = {6 pp.},
annote = {{\textless}p{\textgreater}9189429 {DMoz} data stream mining process named entity extraction feature vectors ontological structure data clustering data classification topic hierarchy software component machine learning Web directory{\textless}/p{\textgreater}} },
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J. Kohler, S. Philippi, M. Specht, and A. Ruegg, "Ontology based text indexing and querying for the semantic Web," Knowledge-Based Systems, vol. 19, iss. 8, pp. 744-54, 2006.
@article{kohler_ontology_2006, title = {Ontology based text indexing and querying for the semantic Web},
volume = {19},
url = {http://dx.doi.org/10.1016/j.knosys.2006.04.015},
abstract = {This publication shows how the gap between the {HTML} based internet and the {RDF} based vision of the semantic web might be bridged, by linking words in texts to concepts of ontologies. Most current search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. However, the indexes do not contain synonyms, cannot differentiate between homonyms (`mouse' as a pointing vs. `mouse' as an animal) and users receive different search results when they use different conjugation forms of the same word. In this publication, we present a system that uses ontologies and Natural Language Processing techniques to index texts, and thus supports word sense disambiguation and the retrieval of texts that contain equivalent words, by indexing them to concepts of ontologies. For this purpose, we developed fully automated methods for mapping equivalent concepts of imported {RDF} ontologies (for this prototype {WordNet,} {SUMO} and {OpenCyc).} These methods will thus allow the seamless integration of domain specific ontologies for concept based information retrieval in different domains. To demonstrate the practical workability of this approach, a set of web pages that contain synonyms and homonyms were indexed and can be queried via a search engine like query frontend. However, the ontology based indexing approach can also be used for other data mining applications such text clustering, relation mining and for searching free text fields in biological databases. The ontology alignment methods and some of the text mining principles described in this publication are now incorporated into the {ONDEX} system http://ondex.sourceforge.net/. {[All} rights reserved Elsevier]},
number = {8},
journal = {{Knowledge-Based} Systems},
author = {J. Kohler and S. Philippi and M. Specht and A. Ruegg},
year = {2006},
keywords = {Indexation, Ontologie, Web sémantique},
pages = {744--54},
annote = {{{\textless}p{\textgreater}Copyright} 2007, The Institution of Engineering and Technology 9426392 0950-7051 ontology text indexing text querying semantic Web {HTML} based Internet {RDF} based vision natural language processing search engines{\textless}/p{\textgreater}} },
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J. Xing and T. Ah-Hwee, "Mining ontological knowledge from domain-specific text documents," , Houston, TX, USA, 2006, p. 4.
@inproceedings{xing_mining_2006, address = {Houston, {TX,} {USA}},
series = {Proceedings. Fifth {IEEE} International Conference on Data Mining},
title = {Mining ontological knowledge from domain-specific text documents},
abstract = {Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called {CRCTOL} for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and lexico-syntactic methods, the knowledge extracted by our system is more concise and contains a richer semantics compared with alternative systems. We conduct a case study wherein {CRCTOL} extracts ontological knowledge, specifically key concepts and semantic relations, from a terrorism domain text collection. Quantitative evaluation, by comparing with a state-of-the-art ontology learning system known as text-to-onto, has shown that {CRCTOL} produces much better precision and recall for both concept and relation extraction, especially from sentences with complex structures},
publisher = {{IEEE} Computer Society},
author = {Jiang Xing and Tan {Ah-Hwee}},
year = {2006},
note = {Copyright 2006, The Institution of Engineering and Technology},
keywords = {Analyse de texte, Approche statistique, Fouille de donnée, Ontologie},
pages = {4 pp.},
annote = {{\textless}p{\textgreater}8857416 ontological knowledge mining domain-specific text document text mining full text parsing statistical method lexico-syntactic method concept extraction relation extraction concept relation concept tuple ontology learning{\textless}/p{\textgreater}} },
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J. Vendetti, Ontology visualizationStandford university, Palo Alto, California.: , 2006.
@misc{vendetti_ontology_2006, address = {Standford university, Palo Alto, California.},
type = {{PDF} Tutorial},
title = {Ontology visualization},
abstract = {This tutorial will focus on several Protégé plug-ins that have been developed specifically for the purpose of visualizing ontologies. The first portion of the tutorial will be a hands-on walk-through of the graph widget tutorial that is available on the Protégé Web site. The graph widget is a custom slot widget plug-in that allows users to graphically create and populate instances of classes. It is very useful as a) an alternative to Protégé Forms for entering instance data, b) as a way to visualize networks of instances, and c) as a way to visualize relationships between instances. The second portion of the tutorial will be a demonstration of the Ontoviz and {TGViz} tab plug-ins. If time permits at the end of the tutorial, there will also be a demonstration of a new visualization plug-in called the Impact \& Alignment Tab, which is a work in progress and will be released in the fall. The target audience for this tutorial are Protégé users with little exposure to the existing visualization plug-ins that are currently available. If users plan to follow the hands-on portion of the tutorial, basic experience with navigating the Protégé user interface will be assumed. Attendees should know how to perform rudimentary tasks such as opening projects and creating new classes, slots, and instances.},
author = {Jennifer Vendetti},
month = jul, year = {2006},
keywords = {Ontologie, Visualisation de l'information},
annote = {{{\textless}p{\textgreater}vendettiJennifer2006.pdf{\textless}/p{\textgreater}}} },
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C. Tempich, H. S. Pinto, and S. Staab, "Ontology engineering revisited : an iterative case study," in The semantic Web : research and applications : 3rd European semantic Web conference, ESWC 2006, Budva, Montenegro, june 11-14, 2006 : proceedings, 2006, pp. 110-124.
@inproceedings{tempich_ontology_2006, series = {Lecture notes in computer science; 4011},
title = {Ontology engineering revisited : an iterative case study},
isbn = {0302-9743},
abstract = {Existing mature ontology engineering approaches are based on some basic assumptions that are often violated in practice, in particular in the Semantic Web. Ontologies often need to be built in a decentralized way, ontologies must be given to a community in a way such that individuals have partial autonomy over them and ontologies have a life cycle that involves an iteration back and forth between construction/modification and use. While recently there have been some initial proposals to consider these issues, they lack the appropriate rigor of mature approaches. i.e. these recent proposals lack the appropriate depth of methodological description, which makes the methodology usable, and they lack a proof of concept by a long-lived case study. In this paper, we revisit mature and new ontology engineering methodologies. We provide an elaborate methodology that takes decentralization, partial autonomy and iteration into account and we demonstrate its proof-of-concept in a real-world cross-organizational case study.},
booktitle = {The semantic Web : research and applications : 3rd European semantic Web conference, {ESWC} 2006, Budva, Montenegro, june 11-14, 2006 : proceedings},
publisher = {Springer},
author = {C. Tempich and H. S. Pinto and S. Staab},
year = {2006},
keywords = {Ontologie},
pages = {110--124} },
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C. Yang, Liang-Chu, and Chun-Yen, "Developing and evaluating an IT specification extraction system," The Electronic Library, vol. 24, iss. 6, pp. 832-846, 2006.
@article{yang_developing_2006, title = {Developing and evaluating an {IT} specification extraction system},
volume = {24},
url = {http://thesius.emeraldinsight.com/10.1108/02640470610714251},
abstract = {Purpose – This paper seeks to establish an extraction system for an information technology {(IT)} product specification named {ITSIES} which combines the natural language process {(NLP)} with the ontology concept and also to evaluate the system's effectiveness in advance. Design/methodology/approach – The development of the system is based on a prototype design and performance validation. This study adopts four classes of {IT} specification {(PC,} Unix server, Monitor, and Printer) that follow {IBM's} and {HP's} product lines as the baseline information in order to construct the extraction system in {GATE} {(General} Architecture for Text Engineering) tools and to examine the {IT} product specification with other brands and patterns. Additionally indices are adopted such as precision, recall, and F-measure as the matrices for evaluating system performance. Findings – The performance shows that the average recall, precision, and F-measure are all over 90 per cent, revealing that the {JAPE} {(Java} Annotation Patterns Engine) grammar rules in the {IT} domain are reasonably good and generally in line with expectations. Originality/value – The paper proposes an integrative framework to examine {IT} product specification information and demonstrates that the system is effective for {IT} application.},
number = {6},
journal = {The Electronic Library},
author = {Chyan Yang and {Liang-Chu} Chen and {Chun-Yen} Peng},
year = {2006},
keywords = {Gestion des connaissances, Ontologie},
pages = {832 -- 846} },
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Sung-Shun, Hsine-Jen, Shang-Chia, and Cheng-Hsin, "Ontology construction for information classification," Expert Systems with Applications, vol. 31, iss. 1, pp. 1-12, 2006.
@article{weng_ontology_2006, title = {Ontology construction for information classification},
volume = {31},
url = {http://www.sciencedirect.com/science/article/B6V03-4H7SYHH-2/2/8f8b3636ec5f2109f13e16d5cb564895},
abstract = {Following the advent of the Internet technology and the rapid growth of its applications, users have spent long periods of time browsing through the ocean of information found in the Internet. This time-consuming hunt, however, makes searching, retrieving, displaying, integrating and maintaining data such arduous tasks. One way to solve this problem is to study the concept behind the Semantic Web in accordance with the principles of ontology. Apart from facilitating the process of information search in the Semantic Web, ontology also provides a method that will enable computers to exchange, search and identify text information. But establishing the ontology necessitates a great deal of expert assistance; manually setting it up would entail a lot of time, not to mention that there are only a handful of experts available. For this reason, using automatic technology to construct the ontology is a subject worth pursuing. This research uses the theory of formal concept analysis to serve as the groundwork in assembling the different levels of ontological concepts in an automated fashion. An ontology diagram will be presented to show the correlation of concepts and their corresponding significance. Moreover, the experiments of this research select a collection of different concepts in an attempt to classify the relationships between documents and concepts. The objective is to develop an automated technology of ontology construction that will support the present information classification system, as well as to upgrade the ontological aspect of the Semantic Web.},
number = {1},
journal = {Expert Systems with Applications},
author = {{Sung-Shun} Weng and {Hsine-Jen} Tsai and {Shang-Chia} Liu and {Cheng-Hsin} Hsu},
year = {2006},
keywords = {Classification, Ontologie, Web sémantique},
pages = {1--12} },
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B. Fortuna, M. Grobelnik, and D. Mladenic, "Background knowledge for ontology construction," , Edinburgh, Scotland, United Kingdom, 2006, pp. 949-950.
@inproceedings{fortuna_background_2006, address = {Edinburgh, Scotland, United Kingdom},
series = {Proceedings of the 15th International Conference on World Wide Web},
title = {Background knowledge for ontology construction},
abstract = {In this paper we describe a solution for incorporating background knowledge into the {OntoGen} system for semi-automatic ontology construction. This makes it easier for different users to construct different and more personalized ontologies for the same domain. To achieve this we introduce a word weighting schema to be used in the document representation. The weighting schema is learned based on the background knowledge provided by user. It is than used by {OntoGen's} machine learning and text mining algorithms.},
publisher = {Association for Computing Machinery, New York, {NY} 10036-5701, United States},
author = {Blaz Fortuna and Marko Grobelnik and Dunja Mladenic},
year = {2006},
keywords = {Ontologie},
pages = {949--950},
annote = {{{\textless}p{\textgreater}Compilation} and indexing terms, Copyright 2007 Elsevier Inc. All rights reserved 072610675962 Text mining algorithms Documents Ontology construction{\textless}/p{\textgreater}},
annote = {{\textless}p{\textgreater}url non fonctionnel enlevé {(Mylène){\textless}/p{\textgreater}}} },
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K. McGarry, "Recent trends in knowledge and data integration for the life sciences," Expert Systems, vol. 23, pp. 330-341, 2006.
@article{mcgarry_recent_2006, title = {Recent trends in knowledge and data integration for the life sciences},
volume = {23},
url = {http://www.ingentaconnect.com/content/bpl/exsy/2006/00000023/00000005/art00009},
abstract = {The bioscience field has seen some spectacular advances in genomic and proteomic technologies that are able to deliver vast quantities of information on cellular activity. Such technologies are of critical importance to biology, medical science and in drug discovery. However, living systems are highly complex and to fully exploit these technologies requires knowledge at many different levels. Information such as genome sequence data, gene expression data, protein-to-protein interactions and metabolic pathways is required to understand the complexity of biological processes. The challenge for bioinformatics is to tackle the problem of fragmentation of knowledge by integrating the many sources of heterogeneous information into a coherent entity. Another problem is that the high level of biological complexity and the fragmented nature of biological research has meant that it is difficult to keep fully conversant with the latest research and discoveries. Progress in one area of biology may have implications for other areas but the dissemination of this knowledge is not straightforward; difficulties such as differences in naming conventions for genes and biological processes has led to confusion and the lack of productivity. This paper reviews the most recent research to overcome the fragmentation problem where technologies such as text mining and ontologies are used within the knowledge discovery process and the specific technical challenges they address.},
journal = {Expert Systems},
author = {Ken {McGarry}},
year = {2006},
keywords = {Bio informatic, Fouille de texte, Ontologie},
pages = {330--341} },
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W. Wong, W. Liu, and M. Bennamoun, "Integrated scoring for spelling error correction, abbreviation expansion and case restoration in dirty text," , Sydney, Australia, 2006, pp. 83-89.
@inproceedings{wong_integrated_2006, address = {Sydney, Australia},
series = {Conferences in Research and Practice in Information Technology Series; Vol. 245},
title = {Integrated scoring for spelling error correction, abbreviation expansion and case restoration in dirty text},
volume = {61},
url = {http://portal.acm.org/citation.cfm?id=1273820&coll=portal&dl=ACM&CFID=31737443&CFTOKEN=41461128},
abstract = {An increasing number of language and speech applications are gearing towards the use of texts from online sources as input. Despite such rise, not much work can be found in the aspect of integrated approaches for cleaning dirty texts from online sources. This paper presents a mechanism of Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration {(ISSAC).} The idea of {ISSAC} was first conceived as part of the text preprocessing phase in an ontology engineering project. Evaluations of {ISSAC} using 400 chat records reveal an improved accuracy of 96.5\% over the existing 74.4\% based on the use of Aspell only.},
author = {Wilson Wong and Wei Liu and Mohammed Bennamoun},
year = {2006},
keywords = {Ontologie},
pages = {83 -- 89} },
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Ibekwe-SanJuan, "Constructing and maintaining knowledge organization tools : a symbolic approach," Journal of Documentation, vol. 62, iss. 2, pp. 229-50, 2006.
@article{fidelia_constructing_2006, title = {Constructing and maintaining knowledge organization tools : a symbolic approach},
volume = {62},
url = {http://dx.doi.org/10.1108/00220410610653316},
abstract = {Purpose - To propose a comprehensive and semi-automatic method for constructing or updating knowledge organization tools such as thesauri. Design/methodology/approach - The paper proposes a comprehensive methodology for thesaurus construction and maintenance combining shallow {NLP} with a clustering algorithm and an information visualization interface. The resulting system {TermWatch,} extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters are mapped onto a {2D} using an integrated visualization tool. Findings - The clusters formed exhibit the different relations necessary to populate a thesaurus or ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations. Practical implications - This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. {TermWatch} can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations. Originality/value - This lies in several points: combined use of linguistic relations with an adapted clustering algorithm, which is scalable and can handle sparse data. The paper proposes a comprehensive approach to semantic relations acquisition whereas existing studies often use one or two approaches. The domain knowledge maps produced by the system represents an added advantage over existing approaches to automatic thesaurus construction in that clusters are formed using semantic relations between domain terms. Thus while offering a meaningful synthesis of the information contained in the original corpus through clustering, the results can be used for knowledge organization tasks (thesaurus building and ontology population) The system also constitutes a platform for performing several knowledge-oriented tasks like science and technology watch, textmining, query refinement},
number = {2},
journal = {Journal of Documentation},
author = {{Ibekwe-SanJuan} Fidelia},
year = {2006},
keywords = {Cluster, Gestion des connaissances, Langage naturel, Ontologie, Thésaurus, Visualisation de l'information},
pages = {229--50},
annote = {{{\textless}p{\textgreater}Copyright} 2006, The Institution of Engineering and Technology 8898529 0022-0418 knowledge organization tools thesauri thesaurus construction natural language processing clustering algorithm information visualization interface complementary linguistic integrated visualization tool information professionals ontology population textmining query refinement{\textless}/p{\textgreater}} },
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V. Uren, P. Cimiano, J. Iria, S. Handschuh, M. Vargas-Vera, E. Motta, and F. Ciravegna, "Semantic annotation for knowledge management : requirements and a survey of the state of the art," Journal of Web semantics, vol. 4, iss. 1, pp. 14-28, 2006.
@article{uren_semantic_2006, title = {Semantic annotation for knowledge management : requirements and a survey of the state of the art},
volume = {4},
issn = {1570-8268},
url = {http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B758F-4HKD000-1-P&_cdi=12925&_user=789722&_orig=search&_coverDate=01%2F31%2F2006&_sk=999959998&view=c&wchp=dGLbVtz-zSkWb&md5=3d45b167960795cdc9ae33841ed0c803&ie=/sdarticle.pdf},
doi = {10.1016/j.websem.2005.10.002},
abstract = {While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging {SemanticWeb,} search, interpretation and aggregation can be addressed by ontology- based semantic mark- up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress. (c) 2005 Elsevier B. V. All rights reserved.},
number = {1},
journal = {Journal of Web semantics},
author = {Victoria Uren and Philipp Cimiano and José Iria and Siegfried Handschuh and Maria {Vargas-Vera} and Enrico Motta and Fabio Ciravegna},
year = {2006},
keywords = {Gestion des connaissances, Ontologie, Web},
pages = {14--28},
annote = {{{\textless}p{\textgreater}urenVictoria2006.pdf{\textless}/p{\textgreater}}} },
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Y. Li and N. Zhong, "Mining ontology for automatically acquiring Web user information needs," Knowledge and Data Engineering, IEEE Transactions on, vol. 18, iss. 4, pp. 554-568, 2006.
@article{yuefeng_li_mining_2006, title = {Mining ontology for automatically acquiring Web user information needs},
volume = {18},
issn = {1041-4347},
url = {http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/69/4039276/04039280.pdf?tp=&isnumber=4039276&arnumber=4039280&punumber=%3Cb%3E%3Cfont%20color=990000%3E69%3C/font%3E%3C/b%3E},
doi = {10.1109/TKDE.2007.23},
abstract = {It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable.},
number = {4},
journal = {Knowledge and Data Engineering, {IEEE} Transactions on},
author = {Yuefeng Li and Ning Zhong},
year = {2006},
keywords = {Ontologie, Web},
pages = {554--568},
annote = {{{\textless}p{\textgreater}liYuefeng2006.pdf{\textless}/p{\textgreater}}} },
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J. F. Sowa, "Ontological semantics," Computational Linguistics, vol. 31, iss. 1, pp. 147-152, 2005.
@article{sowa_ontological_2005, series = {Book reviews},
title = {Ontological semantics},
volume = {31},
url = {http://www.mitpressjournals.org/doi/pdf/10.1162/0891201053630246},
doi = {10.1162/0891201053630246},
abstract = {Ontological Semantics Sergei Nirenburg and Victor Raskin {(University} of Maryland, Baltimore County, and Purdue University) Cambridge, {MA:} The {MIT} Press, 2004, xxi+420 pp; hardbound, {ISBN} 0-262-14086-1, \$50.00, £32.95},
number = {1},
journal = {Computational Linguistics},
author = {John F. Sowa},
month = mar, year = {2005},
keywords = {Ontologie},
pages = {147--152},
annote = {{{\textless}p{\textgreater}sowaJohn2005{\textless}/p{\textgreater}}} },
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V. Kunal, S. Kaarthik, S. Amit, P. Abhijit, O. Swapna, and M. John, "METEOR-S WSDI : a scalable P2P infrastructure of registries for semantic publication and discovery of Web services," Information Technology and Management, vol. 6, pp. 17-39, 2005.
@article{kunal_meteor-s_2005, title = {{METEOR-S} {WSDI} : a scalable {P2P} infrastructure of registries for semantic publication and discovery of Web services},
volume = {6},
url = {http://www.ingentaconnect.com/content/klu/item/2005/00000006/00000001/05277773},
abstract = {Web services are the new paradigm for distributed computing. They have much to offer towards interoperability of applications and integration of large scale distributed systems. To make Web services accessible to users, service providers use Web service registries to publish them. Current infrastructure of registries requires replication of all Web service publications in all Universal Business Registries. Large growth in number of Web services as well as the growth in the number of registries would make this replication impractical. In addition, the current Web service discovery mechanism is inefficient, as it does not support discovery based on the capabilities of the services, leading to a lot of irrelevant matches. Semantic discovery or matching of services is a promising approach to address this challenge. In this paper, we present a scalable, high performance environment for Web service publication and discovery among multiple registries. This work uses an ontology- based approach to organize registries into domains, enabling domain based classification of all Web services. Each of these registries supports semantic publication and discovery of Web services. We believe that the semantic approach suggested in this paper will significantly improve Web service publication and discovery involving a large number of registries. This paper describes the implementation and architecture of the {METEOR-S} Web Service Discovery Infrastructure, which leverages peer to peer computing as a scalable solution.},
journal = {Information Technology and Management},
author = {Verma Kunal and Sivashanmugam Kaarthik and Sheth Amit and Patil Abhijit and Oundhakar Swapna and Miller John},
year = {2005},
keywords = {Ontologie},
pages = {17--39} },
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D. Milward, "Ontology-based interactive information extraction from scientific abstracts," Comparative and Functional Genomics, vol. 6, pp. 67-71, 2005.
@article{milward_ontology-based_2005, title = {Ontology-based interactive information extraction from scientific abstracts},
volume = {6},
url = {http://www.ingentaconnect.com/content/jws/cfg/2005/00000006/F0020001/art00007},
abstract = {Over recent years, there has been a growing interest in extracting information automatically or semi-automatically from the scientific literature. This paper describes a novel ontology-based interactive information extraction {(OBIIE)} framework and a specific {OBIIE} system. We describe how this system enables life scientists to make ad hoc queries similar to using a standard search engine, but where the results are obtained in a database format similar to a pre-programmed information extraction engine. We present a case study in which the system was evaluated for extracting co-factors from {EMBASE} and {MEDLINE.} Copyright © 2005 John Wiley \& Sons, Ltd.},
journal = {Comparative and Functional Genomics},
author = {David Milward},
year = {2005},
keywords = {Extraction d'information, Ontologie},
pages = {67--71} },
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B. Grilheres, S. Canu, C. Beauce, and S. Brunessaux, "A platform for semantic annotations and ontology population using conditional random fields," , Compiegne, France, 2005, pp. 790-3.
@inproceedings{grilheres_platform_2005, address = {Compiegne, France},
series = {Proceedings. The 2005 {IEEE/WIC/ACM} International Conference on Web Intelligence},
title = {A platform for semantic annotations and ontology population using conditional random fields},
abstract = {Ontologies are widely used for organising and sharing knowledge. But elaborating these resources is a heavy and time-consuming task. This paper is two-fold: it describes {EADS} {DCS} text-mining platform, in particular, its service to annotate documents with semantic tags and it presents its extension for incremental learning of ontologies. Domain experts are assisted in the ontology population task by recent machine learning techniques (i.e. conditional random fields). Comparisons are made between annotations from the ontology and from a trained {CRF} model, so as to detect candidate instances. An iterative process controlled by the experts results in knowledge discovery and constitution of an accurate ontology},
publisher = {{IEEE} Comput. Soc},
author = {B. Grilheres and S. Canu and C. Beauce and S. Brunessaux},
year = {2005},
note = {Copyright 2006, {IEE}},
keywords = {Analyse de texte, Fouille de donnée, Ontologie},
pages = {790--3},
annote = {{\textless}p{\textgreater}8747803 semantic annotation ontology population conditional random field knowledge organisation knowledge sharing {EADS} {DCS} text-mining document annotation semantic tags incremental learning machine learning knowledge discovery{\textless}/p{\textgreater}} },
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X. Lacot, Introduction à OWL, un langage XML d’ontologie Web, 2005.
@misc{lacot_introduction_2005, title = {Introduction à {OWL,} un langage {XML} d'ontologie Web},
url = {http://lacot.org/public/introduction_a_owl.pdf},
abstract = {En l'espace de 15 ans, le concept du « web » a fortement évolué. La source disparate de savoirs qu'était le « réseau des réseaux » dans les années 1990 cède progressivement la place, ces dernières années, à l'envie d'une meilleure architecture des échanges de connaissance que permet le web. Ce document présente sommairement quelques concepts du web sémantique, et s'attarde principalement sur le langage d'ontologies web recommandé par le {W3C} : {OWL.}},
author = {Xavier Lacot},
month = jun, year = {2005},
keywords = {Ontologie, Web sémantique},
annote = {{{\textless}p{\textgreater}lacotXavier2005.pdf{\textless}/p{\textgreater}}} },
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P. Cimiano and J. Volker, "Text2Onto : a framework for ontology learning and data-driven change discovery," in Natural language processing and information systems : 10th international conference on applications of natural language to information systems, NLDB 2005, Alicante, Spain, june 15-17 : proceedings, Berlin; Heidelberg, 2005, pp. 227-238.
@inproceedings{cimiano_text2onto_2005, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 3513},
title = {{Text2Onto} : a framework for ontology learning and data-driven change discovery},
isbn = {0302-9743},
abstract = {In this paper we present Text2onto, a framework for ontology learning from textual resources. Three main features distinguish Text20nto from our earlier framework {TextToOnto} as well as other state-of-the-art ontology learning frameworks. First, by representing the learned knowledge at a meta-level in the form of instantiated modeling primitives within a so called Probabilistic Ontology Model {(POM),} we remain independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism. Second, user interaction is a core aspect of Text20nto and the fact that the system calculates a confidence for each learned object allows to design sophisticated visualizations of the {POM.} Third, by incorporating strategies for data-driven change discovery, we avoid processing the whole corpus from scratch each time it changes, only selectively updating the {POM} according to the corpus changes instead. Besides increasing efficiency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.},
booktitle = {Natural language processing and information systems : 10th international conference on applications of natural language to information systems, {NLDB} 2005, Alicante, Spain, june 15-17 : proceedings},
publisher = {Springer},
author = {P. Cimiano and J. Volker},
year = {2005},
keywords = {Apprentissage machine, Découverte de connaissances, Ontologie},
pages = {227--238} },
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R. G. Raskin and M. J. Pan, "Knowledge representation in the semantic web for Earth and environmental terminology (SWEET)," Computers \& Geosciences, vol. 31, iss. 9, pp. 1119-1125, 2005.
@article{raskin_knowledge_2005, title = {Knowledge representation in the semantic web for Earth and environmental terminology {(SWEET)}},
volume = {31},
url = {http://www.sciencedirect.com/science/article/B6V7D-4GG8W0B-1/2/9e383085c8b678c25ba30c1530d9bb16},
abstract = {The semantic web for Earth and environmental terminology {(SWEET)} is an investigation in improving discovery and use of Earth science data, through software understanding of the semantics of web resources. Semantic understanding is enabled through the use of ontologies, or formal representations of technical concepts and their interrelations in a form that supports domain knowledge. The ultimate vision of the semantic web consists of web pages with {XML} namespace tags around terms, enabling search tools to ascertain their meanings by following the link to the defining ontologies. Such a scenario both reduces the number of false hits (where a search returns alternative, unintended meanings of a term) and increases the number of successful hits (where searcher and information provider have a syntax mismatch of the same concept). For {SWEET,} we developed a collection of ontologies using the web ontology language {(OWL)} that include both orthogonal concepts (space, time, Earth realms, physical quantities, etc.) and integrative science knowledge concepts (phenomena, events, etc.). This paper describes the development of a knowledge space for Earth system science and related concepts (such as data properties). Some of the ontology contents are "virtual" by means of an {OWL} wrapper associated with terms in large external databases (including gazetteers and Earthquake databases). We developed a search tool that finds alternative search terms (based on the semantics) and redirects the expanded set of terms to a search engine.},
number = {9},
journal = {Computers \& Geosciences},
author = {Robert G. Raskin and Michael J. Pan},
year = {2005},
keywords = {Classification, Ontologie},
pages = {1119--1125} },
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P. Cimiano, U. Reyle, and J. Saric, "Ontology-driven discourse analysis for information extraction," Data \& Knowledge Engineering, vol. 55, iss. 1, pp. 59-83, 2005.
@article{cimiano_ontology-driven_2005, title = {Ontology-driven discourse analysis for information extraction},
volume = {55},
issn = {{0169-023X}},
abstract = {This paper presents a novel approach to discourse analysis within information extraction systems. It makes use of {DRT} as formal representation of the linguistic context as well as of a domain-specific ontology as a basis to compute conceptual relations between extracted events thus establishing discourse coherence. The approach has been implemented within {GenIE,} an information extraction system with the aim of extracting information about biochemical pathways, about sequences, structures and functions of genomes and proteins. The approach is evaluated against a semantically hand-annotated set of {Swiss-Prot} protein function descriptions and shows very promising results. (c) 2004 Elsevier {B.V.} All rights reserved.},
number = {1},
journal = {Data \& Knowledge Engineering},
author = {P. Cimiano and U. Reyle and J. Saric},
year = {2005},
keywords = {Analyse de discours, Extraction d'information, Langage naturel, Ontologie},
pages = {59--83} },
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J. A. Pretorius, "Visual analysis for ontology engineering," Journal of Visual Languages \& Computing, vol. 16, iss. 4, pp. 359-381, 2005.
@article{pretorius_visual_2005, title = {Visual analysis for ontology engineering},
volume = {16},
url = {http://www.sciencedirect.com/science/article/B6WMM-4FH0D97-2/2/0801066a8aabfdb4edb217c2b762b333},
abstract = {An ontology may be decomposed into a layer of binary fact types and a layer of application specific constraints imposed on these fact types. An ontology base is a large set of binary fact types called lexons. This paper presents {LexoVis,} a lexon visualization tool that addresses the inherent size and scale of ontology bases. {LexoVis} facilitates the analysis of lexons by providing an ordered visual representation. This representation offers overview and detail by employing the graphical fisheye view. Different ordering and clustering heuristics incorporated in {LexoVis} lead to insights not explicit in text-based representations of lexons.},
number = {4},
journal = {Journal of Visual Languages \& Computing},
author = {A. Johannes Pretorius},
year = {2005},
keywords = {Ontologie, Visualisation de l'information},
pages = {359--381} },
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V. Malaisé, "Methodologie linguistique et terminologique pour la structuration d’ontologies," PhD Thesis , 2005.
@phdthesis{malais_methodologie_2005, type = {Thèse de doctorat en linguistique},
title = {Methodologie linguistique et terminologique pour la structuration d'ontologies},
url = {http://www.few.vu.nl/~vmalaise/TheseVMalaise.pdf},
abstract = {Des ressources telles que les terminologies ou les ontologies sont utilisées dans différentes applications, notamment dans la description documentaire et la recherche d’information. Différentes méthodologies ont été proposées pour construire ce type de ressources, que ce soit à partir d’entrevues avec des experts du domaine ou à partir de corpus textuels. Nous nous intéressons dans ce mémoire à l’utilisation de méthodologies existantes dans le domaine du Traitement Automatique des Langues, destinées à la construction d’ontologies à partir de corpus textuels, pour la construction d’un type de ressource particulier : des ontologies différentielles. Ces ontologies sont structurées selon un système d’identité et de différence sémantique entre leurs constituants : les termes du domaine et des catégories dites "de haut niveau". Nous présentons différentes expérimentations qui ont été menées pour éliciter, structurer, définir et interdéfinir les éléments terminologiques pertinents à la réalisation d’une tâche particulière. Notre premier contexte applicatif a été le projet {OPALES,} et nous devions fournir à des anthropologue le vocabulaire conceptuel destiné à annoter des documents audiovisuels traitant de la petite enfance. Nous nous sommes servie du corpus constitué à cette occasion pour tester les méthodologies et outils linguistiques proposés pour l’aide à la construction d’ontologie, et avons défini notre propre chaîne de traitement. Celle-ci, appellée {SODA,} est basée sur l’extraction et l’exploitation d’énoncés définitoires en corpus pour repérer des éléments terminologiques, les structurer et donner des éléments de communauté sémantique permettant de les comparer.},
school = {Université Paris 7 - Denis Diderot},
author = {Véronique Malaisé},
month = oct, year = {2005},
keywords = {Langage naturel, Ontologie},
pages = {158 p.},
annote = {{{\textless}p{\textgreater}malaisVeronique2005\_1.pdf{\textless}/p{\textgreater}}} },
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S. Bergamaschi, "Building a tourism information provider with the MOMIS system," Information Technology \& Tourism, vol. 7, iss. 3-4, pp. 221-238, 2005.
@article{bergamaschi_buildingtourism_2005, title = {Building a tourism information provider with the {MOMIS} system},
volume = {7},
url = {http://www.ingentaconnect.com/content/cog/itt/2005/00000007/F0020003/art00008},
abstract = {The tourism industry is a good candidate for taking up Semantic Web technology. In fact, there are many portals and websites belonging to the tourism domain that promote tourist products (places to visit, food to eat, museums, etc.) and tourist services (hotels, events, etc.), published by several operators (tourist promoter associations, public agencies, etc.). This article presents how the {MOMIS} system may be used for building a tourism information provider by exploiting the tourism information that is available in Internet websites. {MOMIS} {(Mediator} {envirOnment} for Multiple Information Sources) is a mediator framework that performs information extraction and integration from heterogeneous distributed data sources and includes query management facilities to transparently support queries posed to the integrated data sources.},
number = {3-4},
journal = {Information Technology \& Tourism},
author = {Sonia Bergamaschi},
year = {2005},
keywords = {Ontologie},
pages = {221--238} },
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N. Aussenac-Gilles, "Supervised text analysis for ontology and terminology engineering," in Machine Learning for the Semantic Web, 2005, pp. 13-18.
@inproceedings{aussenac-gilles_supervised_2005, title = {Supervised text analysis for ontology and terminology engineering},
abstract = {One of the means to reach a Semantic Web is to add some machine readable meta-data to documents (semantic annotations) and/or to improve the performance of information retrieval applications thanks to the use of semantic resources such as terminologies and ontologies. This position paper promotes the idea that these semantic resources cannot be universal but should rather be domain and even task specific in most cases. Moreover, we assert that their content is all the more relevant that it has been defined from document content analysis. And finally, we advocate in favor of a supervised learning process for their design from well selected texts. We shortly present two contributions, a method to build ontologies from texts and a tool for semantic relation identification, that illustrate these positions.},
booktitle = {Machine Learning for the Semantic Web},
author = {Nathalie {Aussenac-Gilles}},
month = feb, year = {2005},
keywords = {Analyse de texte, Ontologie},
pages = {13--18},
annote = {{{\textless}p{\textgreater}aussenac-gillesNathalie.pdf{\textless}/p{\textgreater}}} },
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Mu-Hee, Soo-Yeon, Dong-Jin, and Sang-Jo, "Automatic classification of web pages based on the concept of domain ontology," , Taipei, Taiwan, 2005, pp. 645-651.
@inproceedings{song_automatic_2005, address = {Taipei, Taiwan},
series = {Proceedings - {Asia-Pacific} Software Engineering Conference, {APSEC}},
title = {Automatic classification of web pages based on the concept of domain ontology},
volume = {2005},
abstract = {The use of ontology in order to provide a mechanism to enable machine reasoning has continuously increased during the last few years. This paper suggests an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontology-based document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two pre-defined categories per given document features. In this paper, Web pages are classified in real time not with experimental data or a learning process, but by similar calculations between the terminology information extracted from Web pages and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined. © 2005 {IEEE.}},
publisher = {{IEEE} Computer Society, Los Alamitos, {CA} 90720-1314, United States},
author = {{Mu-Hee} Song and {Soo-Yeon} Lim and {Dong-Jin} Kang and {Sang-Jo} Lee},
year = {2005},
keywords = {Classification, Ontologie},
pages = {645--651},
annote = {{\textless}p{\textgreater}1530-1362{\textless}/p{\textgreater}},
annote = {{{\textless}p{\textgreater}Compilation} and indexing terms, Copyright 2007 Elsevier Inc. All rights reserved 070910460695 Machine reasoning Terminology information Document classification Domain ontology{\textless}/p{\textgreater}},
annote = {{\textless}p{\textgreater}url non fonctionnel enlevé {(Mylène){\textless}/p{\textgreater}}} },
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M. Abulaish and L. Dey, "Biological ontology enhancement with fuzzy relations : a text-mining framework." 2005, pp. 379-385.
@inproceedings{abulaish_biological_2005, title = {Biological ontology enhancement with fuzzy relations : a text-mining framework},
url = {http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1517875},
abstract = {Domain ontology can help in information retrieval from documents. But ontology is a pre-defined structure with crisp concept descriptions and inter-concept relations. However, due to the dynamic nature of the document repository, ontology should be upgradeable with information extracted through text mining of documents in the domain. This also necessitates that concepts, their descriptions and inter-concept relations should be associated with a degree of fuzziness that will indicate the support for the extracted knowledge according to the currently available resources. Supports may be revised with more knowledge coming in future. This approach preserves the basic structured knowledge format for storing domain knowledge, but at the same time allows for update of information. In this paper, we have proposed a mechanism which initiates text mining with a set of ontological concepts, and thereafter extracts fuzzy relations through text mining. Membership values of relations are functions of frequency of co-occurrence of concepts and relations. We have worked on the {GENIA} corpus and shown how fuzzy relations can be further used for guided information extraction from {MEDLINE} documents.},
author = {Muhammad Abulaish and Lipika Dey},
year = {2005},
keywords = {Ontologie},
pages = {379 -- 385} },
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M. Poesio, "Domain modelling and NLP : formal ontologies, lexica or a bit of both," Applied Ontology, vol. 1, pp. 27-33, 2005.
@article{poesio_domain_2005, title = {Domain modelling and {NLP} : formal ontologies, lexica or a bit of both},
volume = {1},
journal = {Applied Ontology},
author = {Massimo Poesio},
year = {2005},
keywords = {Ontologie},
pages = {27--33},
annote = {{{\textless}p{\textgreater}poesioMassimo2005.pdf{\textless}/p{\textgreater}}} },
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M. Abulaish and L. Dey, "Knowledge enhancement through ontology-guided text mining," in Pattern recognition and machine intelligence : first international conference, PReMI 2005, Kolkata, India, december 20-22, 2005 : proceedings, 2005, pp. 601-604.
@inproceedings{abulaish_knowledge_2005, series = {Lecture notes in computer science; 3776},
title = {Knowledge enhancement through ontology-guided text mining},
url = {http://dx.doi.org/10.1007/11590316_95},
abstract = {In this paper we have proposed a system that performs both ontology-based text information extraction and ontology update using the extracted information. The system employs text-mining techniques to mine information from text documents guided by an underlying ontology. It also enhances the existing ontology with new concepts and their descriptors which may be precise and/ or imprecise, mined from the text. All extracted information related to concepts and concept descriptors are also stored in a structured knowledge base.},
booktitle = {Pattern recognition and machine intelligence : first international conference, {PReMI} 2005, Kolkata, India, december 20-22, 2005 : proceedings},
publisher = {Springer},
author = {Muhammad Abulaish and Lipika Dey},
year = {2005},
keywords = {Fouille de texte, Ontologie},
pages = {601--604} },
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D. B. Lenat, "Applied ontology issues," Applied Ontology, vol. 1, pp. 9-12, 2005.
@article{lenat_applied_2005, title = {Applied ontology issues},
volume = {1},
issn = {15705838 {(Print)} 18758533 {(Online)}},
journal = {Applied Ontology},
author = {Douglas B. Lenat},
year = {2005},
keywords = {Ontologie},
pages = {9--12} },
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J. Tsujii and S. Ananiadou, "Thesaurus or logical ontology, which one do we need for text mining?," Computers and the Humanities, vol. 39, iss. 1, pp. 77-90, 2005.
@article{tsujii_thesaurus_2005, title = {Thesaurus or logical ontology, which one do we need for text mining?},
volume = {39},
doi = {10.1007/s10579-005-2697-0},
abstract = {Ontologies are recognised as important tools, not only for effective and efficient information sharing, but also for information extraction and text mining. In the biomedical domain, the need for a common ontology for information sharing has long been recognised, and several ontologies are now widely used. However, there is confusion among researchers concerning the type of ontology that is needed for text mining , and how it can be used for effective knowledge management, sharing, and integration in biomedicine. We argue that there are several different ways to define an ontology and that, while the logical view is popular for some applications, it may be neither possible nor necessary for text mining. We propose a text-centered approach for knowledge sharing, as an alternative to formal ontologies. We argue that a thesaurus (i.e. an organised collection of terms enriched with relations) is more useful for text mining applications than formal ontologies.},
number = {1},
journal = {Computers and the Humanities},
author = {Junichi Tsujii and Sophia Ananiadou},
year = {2005},
keywords = {Ontologie, Thésaurus},
pages = {77--90},
annote = {{{\textless}p{\textgreater}tsujiiJunichi2005.pdf{\textless}/p{\textgreater}}} },
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P. Buitelaar, P. Cimiano, and B. Magnini, Ontology learning from text : methods, evaluation and applications, Amsterdam ; Washington, DC: IOS Press, 2005.
@book{buitelaar_ontology_2005, address = {Amsterdam ; Washington, {DC}},
series = {Frontiers in artificial intelligence and applications ; 123},
title = {Ontology learning from text : methods, evaluation and applications},
isbn = {1586035231 {(HARDCOVER)}},
publisher = {{IOS} Press},
author = {Paul Buitelaar and Philipp Cimiano and Bernardo Magnini},
year = {2005},
keywords = {Ontologie},
annote = {{\textless}p{\textgreater}edited by Paul Buitelaar, Philipp Cimiano, Bernardo Magnini. ill. ; 24 cm. Ontology learning from text : an overview / An information-theoretic approach to taxonomy extraction for ontology learning / Unsupervised text mining for the learning of {DOGMA-inspired} ontologies / A study on automated relation labelling in ontology learning / Learning taxonomic relations from heterogeneous sources of evidence / An evaluation framework for ontology enrichment / Evaluation of {OntoLearn,} a methodology for automatic learning of domain ontologies / A task-based framework for ontology learning, population and evaluation / Learning Web service ontologies : an automatic extraction method and its evaluation / Exploiting technical terminology for knowledge management / Ontology and information extraction : a necessary symbiosis /{\textless}/p{\textgreater}} },
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N. Aussenac-Gilles and D. Sörgel, "Text analysis for ontology and terminology engineering," Applied Ontology, vol. 1, iss. 1, pp. 35-46, 2005.
@article{aussenac-gilles_text_2005, title = {Text analysis for ontology and terminology engineering},
volume = {1},
issn = {1570-5838 {(Print)} 1875-8533 {(Online)}},
url = {http://iospress.metapress.com/content/e046qf1uwpdaedj7/},
abstract = {After a recent breakthrough in the early 90's, text analysis is acknowledged as one of the promising ways to rapidly build better grounded semantic resources such as terminologies and ontologies. This domain has recently undergone significant evolutions with a massive reference to machine learning algorithms and information extraction techniques together with linguistic- and statistic-based natural language processing. This position paper promotes three main ideas: (i) that highly domain-specific or task-specific, even idiosyncratic ontologies, are very useful, especially when they are linked to broader consensual schemes and they can be built with reasonable effort; (ii) that corpus-based ontologies can capture the perspective of a domain; and (iii) that supervised ontology learning from text makes feasible the development of specialized ontologies adapted for specific uses. We propose the establishment of an inventory of tools for building ontologies from text, give a first classification of such tools, and present an initial review of some recent methods and tools.},
number = {1},
journal = {Applied Ontology},
author = {Nathalie {Aussenac-Gilles} and Dagobert Sörgel},
year = {2005},
keywords = {Analyse de texte, Ontologie, Terminologie},
pages = {35--46},
annote = {{{\textless}p{\textgreater}aussenac-gillesNathalie.pdf{\textless}/p{\textgreater}}} },
-
G. Yanbin and G. Zhao, "Knowledge-based information extraction : a case study of recognizing emails of Nigerian frauds," in Natural language processing and information systems : 10th international conference on applications of natural language to information systems, NLDB 2005, Alicante, Spain, june 15-17, 2005 : proceedings, Alicante, Spain, 2005, pp. 161-172.
@inproceedings{yanbin_knowledge-based_2005, address = {Alicante, Spain},
series = {Lecture notes in computer science; 3513},
title = {Knowledge-based information extraction : a case study of recognizing emails of Nigerian frauds},
abstract = {This paper describes the methodology, process and results of developing application ontology as software specification of the semantics of forensics in the email suspicious of Nigerian frauds. Real life examples of fraud emails are analyzed for evidence and red flags to capture the underlying domain semantics with application ontology of frauds. A model of the natural language structure in regular expressions is developed in the light of the ontology and applied to emails to extract linguistic evidences of frauds. The evaluation of the initial results shows a satisfactory recognition as an automatic fraud alert system. It also demonstrates a methodological significance: the methodical conceptual modeling and specific purpose-driven linguistic modeling are effective in encapsulating and managing their respective needs, perspectives and variability in real life linguistic processing applications},
booktitle = {Natural language processing and information systems : 10th international conference on applications of natural language to information systems, {NLDB} 2005, Alicante, Spain, june 15-17, 2005 : proceedings},
publisher = {{Springer-Verlag}},
author = {Gao Yanbin and G. Zhao},
year = {2005},
note = {Copyright 2005, {IEE}},
keywords = {Langage naturel, Linguistique, Ontologie},
pages = {161--172},
annote = {{\textless}p{\textgreater}8588493 knowledge-based information extraction Nigerian frauds application ontology software specification forensics email natural language structure regular expressions linguistic processing automatic fraud alert system{\textless}/p{\textgreater}} },
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L. Gillam, M. Tariq, and K. Ahmad, "Terminology and the construction of ontology," Terminology, vol. 11, iss. 1, pp. 55-81, 2005.
@article{gillam_terminology_2005, title = {Terminology and the construction of ontology},
volume = {11},
issn = {09299971},
abstract = {This paper discusses a method for corpus-driven ontology design: extracting conceptual hierarchies from arbitrary domain-specific collections of texts. These hierarchies can form the basis for a concept-oriented (onomasiological) terminology collection, and hence may be used as the basis for developing knowledge-based systems using ontology editors. This reference to ontology is explored in the context of collections of terms. The method presented is a hybrid of statistical and linguistic techniques, employing statistical techniques initially to elicit a conceptual hierarchy, which is then augmented through linguistic analysis. The result of such an extraction may be useful in information retrieval, knowledge management, or in the discipline of terminology science itself.},
number = {1},
journal = {Terminology},
author = {Lee Gillam and Mariam Tariq and Khurshid Ahmad},
year = {2005},
keywords = {Analyse de corpus, Langage naturel, Méthodologie, Ontologie},
pages = {55--81},
annote = {{{\textless}p{\textgreater}gillamLee2005.pdf{\textless}/p{\textgreater}}} },
-
V. Malaisé, P. Zweigenbaum, and B. Bachimont, "Mining defining contexts to help structuring differential ontologies," Terminology, vol. 11, iss. 1, pp. 21-53, 2005.
@article{malais_mining_2005, title = {Mining defining contexts to help structuring differential ontologies},
volume = {11},
url = {http://www.ingentaconnect.com/search/download?pub=infobike%3a%2f%2fjbp%2fterm%2f2005%2f00000011%2f00000001%2fart00002&mimetype=application%2fpdf&exitTargetId=1223077728992},
doi = {10.1075/term.11.1.03mal},
abstract = {In this paper, we present an experiment dealing with corpus-based construction of “differential ontologies”, which are organised according to semantic similarity and differential features. We argue that knowledge-rich defining contexts can be useful to help an ontology modeller in his task. We present a method, based on lexico-syntactic patterns, to spot such contexts in a corpus, then identify the terms they relate (definiendum and genus or “characteristics”) and the semantic relation that links them. We also show how potential co-hyponyms can be detected on the basis of shared words in their definiens. We evaluate the extracted defining sentences, semantic relations and co-hyponyms on a test corpus focusing on childhood and on an evaluation corpus about dietetics (both corpora are French). Definition extraction obtains 50\% precision and recall of approximately 40\%. Semantic relation identification reaches an average of 48\% precision, and co-hyponyms 23.5\%. We discuss the results of these experiments and conclude on perspectives for future work.},
number = {1},
journal = {Terminology},
author = {Véronique Malaisé and Pierre Zweigenbaum and Bruno Bachimont},
year = {2005},
keywords = {Fouille de texte, Ontologie},
pages = {21--53},
annote = {{{\textless}p{\textgreater}malaiseVeronique2005.pdf{\textless}/p{\textgreater}}} },
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N. Guarino and M. A. Musen, "Applied ontology : focusing on content," Applied Ontology, vol. 1, pp. 1-5, 2005.
@article{guarino_applied_2005, title = {Applied ontology : focusing on content},
volume = {1},
issn = {15705838 {(Print)} 18758533 {(Online)}},
url = {http://iospress.metapress.com/content/b9rb8k9a8t8acn1x/fulltext.pdf},
journal = {Applied Ontology},
author = {Nicola Guarino and Mark A. Musen},
year = {2005},
keywords = {Ontologie},
pages = {1--5},
annote = {{{\textless}p{\textgreater}guarinoNicola2005.pdf{\textless}/p{\textgreater}}} },
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D. Damle and V. Uren, "Extracting significant words from corpora for ontology extraction," , Banff, Alberta, Canada, 2005, pp. 187-188.
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D. Zhang and W. S. Lee, "Learning to integrate Web taxonomies," Journal of Web semantics, vol. 2, iss. 2, 2005.
@article{zhang_learning_2005, title = {Learning to integrate Web taxonomies},
volume = {2},
url = {www.comp.nus.edu.sg/~leews/publications/dellzhang_ws2004.pdf},
abstract = {We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects from the source taxonomies into categories of the master taxonomy. However, conventional machine learning algorithms totally ignore the availability of the source taxonomies. In fact, source and master taxonomies often have common categories under different names or other more complex semantic overlaps. We introduce two techniques that exploit the semantic overlap between the source and master taxonomies to build better classifiers for the master taxonomy. The first technique, Cluster Shrinkage, biases the learning algorithm against splitting source categories by making objects in the same category appear more similar to each other. The second technique, {Co-Bootstrapping,} tries to facilitate the exploitation of inter-taxonomy relationships by providing category indicator functions as additional features for the objects. Our experiments with real-world Web data show that these proposed add-on techniques can enhance various machine learning algorithms to achieve substantial improvements in performance for taxonomy integration.},
number = {2},
journal = {Journal of Web semantics},
author = {Dell Zhang and Wee Sun Lee},
year = {2005},
keywords = {Apprentissage machine, Classification, Ontologie, Taxonomie, Web sémantique},
annote = {{{\textless}p{\textgreater}zhangDell2005.pdf{\textless}/p{\textgreater}}} },
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V. Kashyap, C. Ramakrishnan, C. Thomas, and A. Sheth, "TaxaMiner : an experimentation framework for automated taxonomy bootstrapping," International Journal of Web and Grid Services, vol. 1, iss. 2, pp. 240-66, 2005.
@article{kashyap_taxaminer_2005, title = {{TaxaMiner} : an experimentation framework for automated taxonomy bootstrapping},
volume = {1},
url = {http://dx.doi.org/10.1504/IJWGS.2005.008322},
abstract = {Construction of domain ontologies on the semantic Web is a human and resource intensive process, efforts to reduce which are crucial for the semantic Web to scale. We present a framework for automated taxonomy construction, that involves: (a) generation of a cluster hierarchy from a document corpus using statistical clustering and {NLP} techniques; (b) extraction of a topic hierarchy from this cluster hierarchy; and (c) assignment of labels to nodes in the topic hierarchy. Metrics for estimating topic hierarchy quality and parameters of an experimentation framework are identified. {MEDLINE®} was the document corpus and {MeSH} thesaurus was the gold standard},
number = {2},
journal = {International Journal of Web and Grid Services},
author = {V. Kashyap and C. Ramakrishnan and C. Thomas and A. Sheth},
year = {2005},
keywords = {Classification, Fouille de donnée, Langage naturel, Ontologie, Thésaurus, Web sémantique},
pages = {240--66},
annote = {{{\textless}p{\textgreater}Copyright} 2006, The Institution of Engineering and Technology 8880213 1741-1106 {TaxaMiner} automated taxonomy bootstrapping domain ontology semantic Web document corpus cluster hierarchy generation statistical clustering natural language processing topic hierarchy extraction {MEDLINE} {MeSH} thesaurus label assignment{\textless}/p{\textgreater}} },
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G. Geleijnse and J. Korst, "Automatic ontology population by googling," , Brussels, Belgium, 2005, p. 120.
@inproceedings{geleijnse_automatic_2005, address = {Brussels, Belgium},
title = {Automatic ontology population by googling},
url = {http://citeseer.ist.psu.edu/geleijnse05automatic.html},
abstract = {We discuss a method to populate ontologies with the use of googled text fragments. We populate an ontology by the use of hand-crafted domain-specific relation patterns, which can be seen as a generalization of Hearst patterns. The algorithm described uses instances of some class returned by Google to find instances of other classes. A case study on populating an ontology on the movie domain is presented as an illustration of the method. We present the algorithm in detail and discuss the results of our work.},
author = {G. Geleijnse and J. Korst},
year = {2005},
keywords = {Ontologie},
pages = {120 --- 126},
annote = {{{\textless}p{\textgreater}Construction} ontologie{\textless}/p{\textgreater}} },
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D. Vrandecic, S. Pinto, C. Tempich, and Y. Sure, "The DILIGENT knowledge processes," Journal of Knowledge Management, vol. 9, iss. 5, pp. 85-96, 2005.
@article{vrandecic_diligent_2005, title = {The {DILIGENT} knowledge processes},
volume = {9},
url = {http://thesius.emeraldinsight.com/10.1108/13673270510622474},
abstract = {Purpose – Aims to present the ontology engineering methodology {DILIGENT,} a methodology focussing on the evolution of ontologies instead of the initial design, thus recognizing that knowledge is a tangible and moving target. Design/methodology/approach – First describes the methodology as a whole, then detailing one of the five main steps of {DILIGENT.} The second part describes case studies, either already performed or planned, and what we learned (or expect to learn) from them. Findings – With the case studies it was discovered the strengths and weaknesses of {DILIGENT.} During the evolution of ontologies, arguments need to be exchanged about the suggested changes. Identifies those kind of arguments which work best for the discussion of ontology changes. Research implications – {DILIGENT} recognizes ontology engineering methodologies like {OnToKnowledge} or Methontology as proven useful for the initial design, but expands them with its strong focus on the user-centric further development of the ontology and the provided integration of automatic agents in the process of ontology evolution. Practical implications – With {DILIGENT} the experience distilled from a number of case studies and offers the knowledge manager a methodology to work in an ever-changing environment. Originality/value – {DILIGENT} is the first methodology to put focus not on the initial development of the ontology, but on the user and his usage of the ontology, and on the changes introduced by the user. We take the user's own view seriously and enable feedback towards the evolution of the ontology, stressing the ontology's role as a shared conceptualisation.},
number = {5},
journal = {Journal of Knowledge Management},
author = {Denny Vrandecic and Sofia Pinto and Christoph Tempich and York Sure},
year = {2005},
keywords = {Méthodologie, Ontologie},
pages = {85 -- 96} },
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R. Mizoguchi, "Advanced course of ontological engineering," New generation computing, vol. 22, iss. 2, 2004.
@article{mizoguchi_advanced_2004, series = {Ontological engineering / Ontology engineering: tutorial},
title = {Advanced course of ontological engineering},
volume = {22},
url = {http://www.ei.sanken.osaka-u.ac.jp/pub/miz/Part3V3.pdf},
abstract = {This tutorial course describes the current state of the art of ontological engineering which is a successor of knowledge engineering. It covers theory, tools and applications and consists of three parts: Part 1 is an introduction to ontological engineering, Part 2 describes ontology development, languages and tools, and Part 3 is an advanced course dealing with philosophical issues of ontology design together with detailed guidelines of ontology development. Part 3 also presents a success story of ontological engineering with the deployment result in a company. The philosophy behind this tutorial is that ontological engineering is viewed as a challenge to enabling knowledge sharing and reuse which knowledge engineering failed to realize. Therefore, one of the major topics dealt with in this tutorial is to explain what an ontology should be while explaining how it is understood currently.},
number = {2},
journal = {New generation computing},
author = {Riichiro Mizoguchi},
year = {2004},
keywords = {Ontologie},
annote = {{{\textless}p{\textgreater}mizoguchiRiichiro2003\_2.pdf{\textless}/p{\textgreater}}} },
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F. Ginter, S. Pyysalo, J. Boberg, J. Jarvinen, and T. Salakoski, "Ontology-based feature transformations : a data-driven approach," in Advances in natural language processing : 4th international conference, EsTAL 2004, Alicante, Spain, october 20-22, 2004 : proceedings, Heidelberg, 2004, pp. 279-290.
@inproceedings{ginter_ontology-based_2004, address = {Heidelberg},
series = {Lecture notes in computer science; 3230. Lecture notes in artificial intelligence},
title = {Ontology-based feature transformations : a data-driven approach},
abstract = {We present a novel approach to incorporating semantic information to the problems of natural language processing, in particular to the document classification task. The approach builds on the intuition that semantic relatedness of words can be viewed as a non-static property of the words that depends on the particular task at hand. The semantic relatedness information is incorporated using feature transformations, where the transformations are based on a feature ontology and on the particular classification task and data. We demonstrate the approach on the problem of classifying {MEDLINE-indexed} documents using the {MeSH} ontology, The results suggest that the method is capable of improving the classification performance on most of the datasets. © {Springer-Verlag} Berlin Heidelberg 2004.},
booktitle = {Advances in natural language processing : 4th international conference, {EsTAL} 2004, Alicante, Spain, october 20-22, 2004 : proceedings},
publisher = {Springer},
author = {Filip Ginter and Sampo Pyysalo and Jorma Boberg and Jouni Jarvinen and Tapio Salakoski},
year = {2004},
keywords = {Classification, Ontologie},
pages = {279--290},
annote = {{{\textless}p{\textgreater}Compilation} and indexing terms, Copyright 2007 Elsevier Inc. All rights reserved 05329284612 Feature transformation Classification datasets Training sets Specialization{\textless}/p{\textgreater}} },
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A. Gomez-Perez and D. Manzano-Macho, "An overview of methods and tools for ontology learning from texts," Knowledge Engineering Review, vol. 19, iss. 3, pp. 187-212, 2004.
@article{gomez-perez_overview_2004, title = {An overview of methods and tools for ontology learning from texts},
volume = {19},
issn = {0269-8889},
abstract = {Ontology learning aims at reducing the time and efforts in the ontology development process. In recent years, several methods and tools have been proposed to speed up this process using different sources of information and different techniques. In this paper, we have reviewed 13 methods and 14 tools for semi-automatically building ontologies from texts and their relationships with the techniques each method follows. The methods have been grouped according to the main techniques followed and three groups have been identified: one based on linguistics, one on statistics, and one on machine learning. Regarding the tools, the criterion for grouping them, which has been the main aim of the tool, is to distinguish what elements of the ontology can be learned with each tool. According to this, we have identified three kinds of tools: tools for learning relations, tools for learning new concepts, and assisting tools for building up taxonomies.},
number = {3},
journal = {Knowledge Engineering Review},
author = {A. {Gomez-Perez} and D. {Manzano-Macho}},
year = {2004},
keywords = {Classification, Ontologie, Web sémantique},
pages = {187--212} },
-
L. Soo-yeon, S. Mu-hee, and L. Sang-jo, "Domain-specific ontology construction by terminology processing," Journal of KISS: Software and Applications, vol. 31, iss. 3, pp. 353-60, 2004.
@article{soo-yeon_domain-specific_2004, title = {Domain-specific ontology construction by terminology processing},
volume = {31},
abstract = {Ontology defines the terms used in a specific domain and the relationships between them and represents them as hierarchical taxonomy. The present paper proposes a semiautomatic domain-specific ontology construction method based on terminology processing. For this purpose, it presents an algorithm to extract terminology according to the noun/suffix pattern of terminology in domain texts and find their hierarchical structure. The experiment was carried out using pharmacy-related documents. As singleton terminology with noun/suffix were identified, the average accuracy was 92.57\%. In case of multiword terminology, the average accuracy was 66.64\%. The constructed ontology forms natural semantic clusters with based on suffices and semantic information, so can be utilized in approaches to specific knowledge such as information look-up or as the base of inference to improve searching abilities},
number = {3},
journal = {Journal of {KISS:} Software and Applications},
author = {Lim Soo-yeon and Song Mu-hee and Lee Sang-jo},
year = {2004},
keywords = {Ontologie},
pages = {353--60},
annote = {{{\textless}p{\textgreater}Copyright} 2005, {IEE} 8228113 1229-6848 ontology construction terminology processing hierarchical taxonomy pharmacy-related documents multiword terminology natural semantic clusters information look-up{\textless}/p{\textgreater}} },
-
J. Makkonen, H. Ahonen-Myka, and M. Salmenkivi, "Simple semantics in topic detection and tracking : special issue on ECIR," Information Retrieval, vol. 7, pp. 347-368, 2004.
@article{makkonen_simple_2004, title = {Simple semantics in topic detection and tracking : special issue on {ECIR}},
volume = {7},
url = {http://www.ingentaconnect.com/content/klu/inrt/2004/00000007/F0020003/05264860},
abstract = {Topic Detection and Tracking {(TDT)} is a research initiative that aims at techniques to organize news documents in terms of news events. We propose a method that incorporates simple semantics into {TDT} by splitting the term space into groups of terms that have the meaning of the same type. Such a group can be associated with an external ontology. This ontology is used to determine the similarity of two terms in the given group. We extract proper names, locations, temporal expressions and normal terms into distinct sub-vectors of the document representation. Measuring the similarity of two documents is conducted by comparing a pair of their corresponding sub-vectors at a time. We use a simple perceptron to optimize the relative emphasis of each semantic class in the tracking and detection decisions. The results suggest that the spatial and the temporal similarity measures need to be improved. Especially the vagueness of spatial and temporal terms needs to be addressed.},
journal = {Information Retrieval},
author = {J. Makkonen and H. {Ahonen-Myka} and M. Salmenkivi},
year = {2004},
keywords = {Extraction d'information, Ontologie},
pages = {347--368} },
-
S. Staab and R. Studer, Handbook on ontologies, Berlin ; New York: Springer-Verlag, 2004.
@book{staab_handbookontologies_2004, address = {Berlin ; New York},
series = {International handbooks on information systems},
title = {Handbook on ontologies},
isbn = {3540408347 {(ALK.} {PAPER)}},
abstract = {An ontology is a description (like a formal specification of a program) of concepts and relationships that can exist for an agent or a community of agents. The concept is important for the purpose of enabling knowledge sharing and reuse. The Handbook on Ontologies provides a comprehensive overview of the current status and future prospectives of the field of ontologies. The handbook demonstrates standards that have been created recently, it surveys methods that have been developed and it shows how to bring both into practice of ontology infrastructures and applications that are the best of their kind.},
publisher = {{Springer-Verlag}},
author = {Steffen Staab and Rudi Studer},
year = {2004},
keywords = {Design, Ontologie} },
-
L. Soo-Yeon, S. Mu-Hee, S. Ki-Jun, and L. Sang-Jo, "Domain ontology construction based on semantic relation information of terminology," , Busan, South Korea, 2004, pp. 2213-17.
@inproceedings{soo-yeon_domain_2004, address = {Busan, South Korea},
series = {{IECON} 2004 - 30th Annual Conference of {IEEE} Industrial Electronics Society {(IEEE} Cat. {No.04CH37609)}},
title = {Domain ontology construction based on semantic relation information of terminology},
volume = {Vol. 3},
abstract = {Ontology is an explicit specification of a conceptualization. That is, ontology is a description of the concepts and relationships that can exist for an agent or a community of agents. This study suggests a method of constructing domain ontology using terminology processing and applies the method to document retrieval. In order to construct ontology, if proposes an algorithm that classifies the patients of nouns and suffices which compose terminology, in domain texts, extracts terminology, and build a hierarchical structure. The experiment used documents related to pharmacy, and the algorithm showed accuracy of 92.57\% for singleton terms and 66.64\% for multi-word terms on the average. Constructed ontology, which forms natural groups of senses centering on specific nouns or suffices composing the terminology with semantic information, can be utilized in approaching the knowledge of special areas such as document retrieval. According to the result of document retrieval based on the constructed ontology, the system improved in precision and recall compared to traditional keyword-based document retrieval},
publisher = {{IEEE}},
author = {Lim {Soo-Yeon} and Song {Mu-Hee} and Son {Ki-Jun} and Lee {Sang-Jo}},
year = {2004},
note = {Copyright 2005, {IEE}},
keywords = {Classification, Ontologie, Recherche d'information, Web sémantique},
pages = {2213--17},
annote = {{\textless}p{\textgreater}8597350 domain ontology construction semantic relation information explicit specification terminology processing keyword-based document retrieval patient hierarchical structure singleton term multi-word term{\textless}/p{\textgreater}} },
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M. Horridge, H. Knublauch, A. Rector, R. Stevens, and C. Wroe, "A practical guide to building OWL ontologies using the protege-OWL plugin and CO-ODE tools edition 1.0," , 2004.
@article{horridge_practical_2004, title = {A practical guide to building {OWL} ontologies using the {protege-OWL} plugin and {CO-ODE} tools edition 1.0},
url = {http://www.co-ode.org/resources/tutorials/ProtegeOWLTutorial.pdf},
author = {Matthew Horridge and Holger Knublauch and Alan Rector and Robert Stevens and Chris Wroe},
month = aug, year = {2004},
keywords = {Ontologie},
annote = {{{\textless}p{\textgreater}horridgeMatthew2004.pdf{\textless}/p{\textgreater}}} },
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D. Richards, "Addressing the ontology acquisition bottleneck through reverse ontological engineering," Knowledge and information systems, vol. 6, iss. 4, pp. 402-427, 2004.
@article{richards_addressingontology_2004, title = {Addressing the ontology acquisition bottleneck through reverse ontological engineering},
volume = {6},
url = {http://www.ingentaconnect.com/content/klu/10115/2004/00000006/00000004/art00003},
abstract = {The use of ontologies in knowledge engineering arose as a solution to the difficulties associated with acquiring knowledge, commonly referred to as the knowledge acquisition bottleneck. The knowledge-level model represented in an ontology provides a much more structured and principled approach compared with earlier transfer-of-symbolic-knowledge approaches but brings with it a new problem, which can be termed the ontology-acquisition (and maintenance) bottleneck. Each ontological approach offers a different structure, different terms and different meanings for those terms. The unifying theme across approaches is the considerable effort associated with developing, validating and connecting ontologies. We propose an approach to engineering ontologies by retrospectively and automatically discovering them from existing data and knowledge sources in the organization. The method offered assists in the identification of similar and different terms and includes strategies for developing a shared ontology. The approach uses a human-centered, concept-based knowledge processing technique, known as formal concept analysis, to generate an ontology from examples. To assist classification of examples and to identify the salient features of the example, we use a rapid and incremental knowledge acquisition and representation technique, known as ripple-down rules. The method can be used as an alternative or complement to other approaches.},
number = {4},
journal = {Knowledge and information systems},
author = {Debbie Richards},
year = {2004},
keywords = {Ontologie},
pages = {402--427},
annote = {{{\textless}p{\textgreater}Construction} ontologie{\textless}/p{\textgreater}} },
-
G. Lame, "Using NLP techniques to identify legal ontology components: concepts and relations," Artificial Intelligence and Law, vol. 12, iss. 4, pp. 379-96, 2004.
@article{lame_using_2004, title = {Using {NLP} techniques to identify legal ontology components: concepts and relations},
volume = {12},
url = {http://dx.doi.org/10.1007/s10506-005-4160-3},
abstract = {A method to identify ontology components is presented in this article. The method relies on natural language processing {(NLP)} techniques to extract concepts and relations among these concepts. This method is applied in the legal field to build an ontology dedicated to information retrieval. Legal texts on which the method is performed are carefully chosen as describing and conceptualizing the legal domain. We suggest that this method can help legal ontology designers and may be used while building ontologies dedicated to other tasks than information retrieval},
number = {4},
journal = {Artificial Intelligence and Law},
author = {G. Lame},
year = {2004},
keywords = {Langage naturel, Ontologie, Recherche d'information},
pages = {379--96},
annote = {{{\textless}p{\textgreater}Copyright} 2006, The Institution of Engineering and Technology 9059803 0924-8463 natural language processing technique legal ontology component concept extraction relation extraction information retrieval legal text processing{\textless}/p{\textgreater}} },
-
I. Spasic and S. Ananiadou, "Using automatically learnt verb selectional preferences for classification of biomedical terms," Journal of Biomedical Informatics, vol. 37, iss. 6, pp. 483-97, 2004.
@article{spasic_using_2004, title = {Using automatically learnt verb selectional preferences for classification of biomedical terms},
volume = {37},
url = {http://dx.doi.org/10.1016/j.jbi.2004.08.002},
abstract = {In this paper, we present an approach to term classification based on verb selectional patterns {(VSPs),} where such a pattern is defined as a set of semantic classes that could be used in combination with a given domain-specific verb. {VSPs} have been automatically learnt based on the information found in a corpus and an ontology in the biomedical domain. Prior to the learning phase, the corpus is terminologically processed: term recognition is performed by both looking up the dictionary of terms listed in the ontology and applying the {C/NC-value} method for on-the-fly term extraction. Subsequently, domain-specific verbs are automatically identified in the corpus based on the frequency of occurrence and the frequency of their co-occurrence with terms. {VSPs} are then learnt automatically for these verbs. Two machine learning approaches are presented. The first approach has been implemented as an iterative generalisation procedure based on a partial order relation induced by the domain-specific ontology. The second approach exploits the idea of genetic algorithms. Once the {VSPs} are acquired, they can be used to classify newly recognised terms co-occurring with domain-specific verbs. Given a term, the most frequently co-occurring domain-specific verb is selected. Its {VSP} is used to constrain the search space by focusing on potential classes of the given term. A nearest-neighbour approach is then applied to select a class from the constrained space of candidate classes. The most similar candidate class is predicted for the given term. The similarity measure used for this purpose combines contextual, lexical, and syntactic properties of terms},
number = {6},
journal = {Journal of Biomedical Informatics},
author = {I. Spasic and S. Ananiadou},
year = {2004},
keywords = {Classification, Ontologie},
pages = {483--97},
annote = {{{\textless}p{\textgreater}Copyright} 2005, {IEE} 8282669 1532-0464 biomedical terms classification verb selectional patterns semantic class domain-specific verb corpus processing ontology biomedical domain term recognition dictionary {C-NC-value} method on-the-fly term extraction machine learning iterative generalisation procedure partial order relation genetic algorithms nearest-neighbour approach contextual property lexical property syntactic property{\textless}/p{\textgreater}} },
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Y. Shahar, O. Young, E. Shalom, M. Galperin, A. Mayaffit, R. Moskovitch, and A. Hessing, "A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools," Journal of Biomedical Informatics, vol. 37, iss. 5, pp. 325-344, 2004.
@article{shahar_framework_2004, title = {A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools},
volume = {37},
url = {http://www.sciencedirect.com/science/article/B6WHD-4D6356K-1/2/7e6e937497e73d81b63ff680ad9cdda6},
abstract = {Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, not in a formal, executable format, and are not easily accessible to clinicians at the point of care. We introduce a Web-based, modular, distributed architecture, the Digital Electronic Guideline Library {(DeGeL),} which facilitates gradual conversion of clinical guidelines from text to a formal representation in chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application, and retrospective quality assessment. The {DeGeL} hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation, and a formal representation. These formats support increasingly sophisticated computational tasks. The {DeGeL} tools for support of guideline-based care operate, at some level, on all guideline ontologies. We have demonstrated the feasibility of the architecture and the tools for several guideline ontologies, including Asbru and {GEM.}},
number = {5},
journal = {Journal of Biomedical Informatics},
author = {Yuval Shahar and Ohad Young and Erez Shalom and Maya Galperin and Alon Mayaffit and Robert Moskovitch and Alon Hessing},
year = {2004},
keywords = {Ontologie},
pages = {325--344},
annote = {{{\textless}p{\textgreater}Lire} Application ontologie Prototype = {DeGeL} {(Digital} Electronic Guideline Library) Biomedical -- Guidelines{\textless}/p{\textgreater}} },
-
J. Geller, Y. Perl, and J. Lee, "Editorial : ontology challenges : a thumbnail historical perspective," Knowledge and information systems, vol. 6, iss. 4, pp. 375-379, 2004.
@article{geller_editorial_2004, title = {Editorial : ontology challenges : a thumbnail historical perspective},
volume = {6},
url = {http://springerlink.metapress.com/content/l9y5y8nlhrq9nlly/fulltext.pdf},
number = {4},
journal = {Knowledge and information systems},
author = {James Geller and Yehoshua Perl and Jintae Lee},
year = {2004},
keywords = {Ontologie},
pages = {375--379},
annote = {{{\textless}p{\textgreater}gellerJames2004.pdf{\textless}/p{\textgreater}}} },
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H. Pan, L. Zuo, V. Choudhary, Z. Zhang, S. H. Leow, F. T. Chong, Y. Huang, V. W. S. Ong, B. Mohanty, S. L. Tan, S. P. T. Krishnan, and V. B. Bajic, "Dragon TF Association Miner : a system for exploring transcription factor associations through text-mining," Nucleic Acids Research, vol. 32, p. w230-w234, 2004.
@article{pan_dragon_2004, title = {Dragon {TF} Association Miner : a system for exploring transcription factor associations through text-mining},
volume = {32},
url = {http://www.ingentaconnect.com/content/oup/nar/2004/00000032/A00100s2/w230},
abstract = {We present Dragon {TF} Association Miner {(DTFAM),} a system for text-mining of {PubMed} documents for potential functional association of transcription factors {(TFs)} with terms from Gene Ontology {(GO)} and with diseases. {DTFAM} has been trained and tested in the selection of relevant documents on a manually curated dataset containing {\textgreater}3000 {PubMed} abstracts relevant to transcription control. On our test data the system achieves sensitivity of 80\% with specificity of 82\%. {DTFAM} provides comprehensive tabular and graphical reports linking terms to relevant sets of documents. These documents are color-coded for easier inspection. {DTFAM} complements the existing biological resources by collecting, assessing, extracting and presenting associations that can reveal some of the not so easily observable connections among the entities found which could explain the functions of {TFs} and help decipher parts of gene transcriptional regulatory networks. {DTFAM} summarizes information from a large volume of documents saving time and making analysis simpler for individual users. {DTFAM} is freely available for academic and non-profit users at {http://research.i2r.a-star.edu.sg/DRAGON/TFAM/.}},
journal = {Nucleic Acids Research},
author = {Hong Pan and Li Zuo and Vidhu Choudhary and Zhuo Zhang and Shoi Houi Leow and Fui Teen Chong and Yingliang Huang and Victor Wui Siong Ong and Bijayalaxmi Mohanty and Sin Lam Tan and S. P. T. Krishnan and Vladimir B. Bajic},
year = {2004},
keywords = {Ontologie},
pages = {W230--W234} },
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K. Allan, "Aristotle’s footprints in the linguist’s garden," Language Sciences, vol. 26, iss. 4, pp. 317-342, 2004.
@article{allan_aristotles_2004, title = {Aristotle's footprints in the linguist's garden},
volume = {26},
url = {http://www.sciencedirect.com/science/article/B6VD2-4B2CH2P-4/2/b9cde8e77533238dfd554a7c3a66a563},
doi = {10.1016/j.langsci.2003.05.001},
abstract = {For Aristotle, language is {(A)} a symbolic system that represents {(B)} the world of our experience as it is contained within the mind. He believed {(C)} that the world is external to human beings, who are all capable of {(D)} perceiving the same things within it. Finally, {(E)} Aristotle was only interested in form as a corollary of function. {(A-E)} have given rise to different developments in linguistics. {(A)} is a premise for all linguists, but has been developed, perhaps to its limits, in {post-Fregean} semantics. Since the last quarter of the 20th century, {(B)} has been pursued by cognitive linguists. {(C)} was taken up by the speculative grammarians of the late middle ages. The rationalists of the 17th and 18th centuries took up {(D),} revising the interpretation of their speculative precursors to seek universal grammar in the rational minds of the human beings perceiving the world around them. Chomsky reinterprets the rationalist doctrine to seek universal grammar in the human mind while eschewing the relevance of human perception of anything other than linguistic input. Functional linguistics has picked up on {(E).} So, today's formal linguists, cognitivists, functionalists, and Chomskyites may often be at odds with each other, but all tread in Aristotle's footprints.},
number = {4},
journal = {Language Sciences},
author = {Keith Allan},
year = {2004},
keywords = {Linguistique, Philosophie},
pages = {317--342},
annote = {{{\textless}p{\textgreater}allanKeith2004.pdf{\textless}/p{\textgreater}}} },
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R. Navigli and P. Velardi, "Learning domain ontologies from document warehouses and dedicated websites," Computational linguistics, vol. 30, iss. 2, 2004.
@article{navigli_learning_2004, title = {Learning domain ontologies from document warehouses and dedicated websites},
volume = {30},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.124&rep=rep1&type=pdf},
abstract = {We present a method and a tool, {OntoLearn,} aimed at the extraction of domain ontologies from web sites, and more generally from documents shared among the members of virtual organizations. {OntoLearn} first extracts a domain terminology from available documents. Then, complex domain terms are semantically interpreted and arranged in a hierarchical fashion. Finally, a general purpose ontology, i.e. {WordNet,} is trimmed and enriched with the detected domain concepts. The major novel aspect of this approach is semantic interpretation, that is, the association of a complex concept with a complex term. This involves finding the appropriate {WordNet} concept for each word of a terminological string and the appropriate conceptual relations that hold among the concept components. Semantic interpretation is based on a new {WSD} algorithm, called structural semantic interconnections.},
number = {2},
journal = {Computational linguistics},
author = {Roberto Navigli and Paola Velardi},
year = {2004},
keywords = {Ontologie, Web},
annote = {{{\textless}p{\textgreater}navigliRoberto2004.pdf{\textless}/p{\textgreater}}} },
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R. Mizoguchi, "Ontology development, tools and languages," New generation computing, vol. 22, iss. 1, pp. 61-96, 2004.
@article{mizoguchi_ontology_2004, series = {Ontological engineering / Ontology engineering : tutorial},
title = {Ontology development, tools and languages},
volume = {22},
url = {http://www.ei.sanken.osaka-u.ac.jp/pub/miz/Part2V3.pdf},
abstract = {This tutorial course describes the current state of the art of ontological engineering which is a successor of knowledge engineering. It covers theory, tools and applications and consists of three parts: Part 1 is an introduction to ontological engineering, Part 2 describes ontology development, languages and tools, and Part 3 is an advanced course dealing with philosophical issues of ontology design together with detailed guidelines of ontology development. Part 3 also presents a success story of ontological engineering with the deployment result in a company. The philosophy behind this tutorial is that ontological engineering is viewed as a challenge to enabling knowledge sharing and reuse which knowledge engineering failed to realize. Therefore, one of the major topics dealt with in this tutorial is to explain what an ontology should be while explaining how it is understood currently.},
number = {1},
journal = {New generation computing},
author = {Riichiro Mizoguchi},
year = {2004},
keywords = {Ontologie},
pages = {61--96},
annote = {{{\textless}p{\textgreater}mizoguchiRiichiro2003\_1.pdf{\textless}/p{\textgreater}}} },
-
S. Nirenburg and V. Raskin, Ontological semantics, Cambridge, Mass.: MIT Press, 2004.
@book{nirenburg_ontological_2004, address = {Cambridge, Mass.},
series = {Language, Speech, and Communication},
title = {Ontological semantics},
isbn = {0-262-14086-1},
publisher = {{MIT} Press},
author = {Sergei Nirenburg and Victor Raskin},
year = {2004},
keywords = {Ontologie} },
-
N. F. Noy and M. Klein, "Ontology evolution : not the same as schema evolution," Knowledge and information systems, vol. 6, iss. 4, pp. 428-440, 2004.
@article{noy_ontology_2004, title = {Ontology evolution : not the same as schema evolution},
volume = {6},
abstract = {As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.},
number = {4},
journal = {Knowledge and information systems},
author = {Natalya F. Noy and Michel Klein},
year = {2004},
keywords = {Ontologie},
pages = {428--440},
annote = {{{\textless}p{\textgreater}noyNatalya2004.pdf{\textless}/p{\textgreater}}} },
-
Zhang-Zhi, I. Mani, V. Hermoso, H. Liu, and C. H. Wu, "iProLINK : an integrated protein resource for literature mining," Computational Biology and Chemistry, vol. 28, iss. 5-6, pp. 409-416, 2004.
@article{hu_iprolink_2004, title = {{iProLINK} : an integrated protein resource for literature mining},
volume = {28},
url = {http://www.sciencedirect.com/science/article/B73G2-4DTT2HY-5/2/9a93751a242c996dfc3c902449ca671c},
abstract = {The exponential growth of large-scale molecular sequence data and of the {PubMed} scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for training and benchmarking, the Protein Information Resource {(PIR)} has developed a resource for protein literature {mining--iProLINK} (integrated Protein Literature {INformation} and Knowledge). As {PIR} focuses its effort on the curation of the {UniProt} protein sequence database, the goal of {iProLINK} is to provide curated data sources that can be utilized for text mining research in the areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development. The data sources for bibliography mapping and annotation extraction include mapped citations {(PubMed} {ID} to protein entry and feature line mapping) and annotation-tagged literature corpora. The latter includes several hundred abstracts and full-text articles tagged with experimentally validated post-translational modifications {(PTMs)} annotated in the {PIR} protein sequence database. The data sources for entity recognition and ontology development include a protein name dictionary, word token dictionaries, protein name-tagged literature corpora along with tagging guidelines, as well as a protein ontology based on {PIRSF} protein family names. {iProLINK} is freely accessible at , with hypertext links for all downloadable files.},
number = {5-6},
journal = {Computational Biology and Chemistry},
author = {{Zhang-Zhi} Hu and Inderjeet Mani and Vincent Hermoso and Hongfang Liu and Cathy H. Wu},
year = {2004},
keywords = {Langage naturel, Ontologie},
pages = {409--416} },
-
K. Sycara and M. Paolucci, "Ontologies in agent architectures." Berlin ; New York: Springer, 2004, pp. 343-363.
@incollection{sycara_ontologies_2004, address = {Berlin ; New York},
title = {Ontologies in agent architectures},
isbn = {3540408347},
booktitle = {Handbook on Ontologies},
publisher = {Springer},
author = {Katia Sycara and Massimo Paolucci},
year = {2004},
keywords = {Ontologie},
pages = {343--363} },
-
H. Han, "Learning rules for conceptual structure on the Web : special issue on Web content mining," Journal of Intelligent Information Systems, vol. 22, iss. 3, pp. 237-256, 2004.
@article{han_learning_2004, title = {Learning rules for conceptual structure on the Web : special issue on Web content mining},
volume = {22},
url = {http://www.ingentaconnect.com/content/klu/jiis/2004/00000022/00000003/05271105},
abstract = {This paper presents an infrastructure and methodology to extract conceptual structure from Web pages, which are mainly constructed by {HTML} tags and incomplete text. Human beings can easily read Web pages and grasp an idea about the conceptual structure of underlying data, but cannot handle excessive amounts of data due to lack of patience and time. However, it is extremely difficult for machines to accurately determine the content of Web pages due to lack of understanding of context and semantics. Our work provides a methodology and infrastructure to process Web data and extract the underlying conceptual structure, in particular relationships between ontological concepts using Inductive Logic Programming in order to help with automating the processing of the excessive amount of Web data by capturing its conceptual structures.},
number = {3},
journal = {Journal of Intelligent Information Systems},
author = {H. Han},
year = {2004},
keywords = {Découverte de connaissances, Extraction d'information, Ontologie},
pages = {237--256} },
-
M. Dittenbach, H. Berger, and D. Merll, "Improving domain ontologies by mining semantics from text," in Proceedings of the First Asian-Pacific Conference on Conceptual Modelling : 2004, Dunedin, New Zealand, Darlinghurst, Australia, 2004, pp. 91-100.
@inproceedings{dittenbach_improving_2004, address = {Darlinghurst, Australia},
series = {Conferences in Research and Practice in Information Technology Series; 59},
title = {Improving domain ontologies by mining semantics from text},
volume = {31},
abstract = {The creation and maintenance of domain ontologies is a costly and time-consuming task. With the advent of ontologies being used in many different fields of computer science, developing appropriate algorithms and methods to support or automatize ontology engineering have become an increasingly important goal. Hence, we present a connectionist approach to visualize semantic relations inherent in free-from text documents related to a specific domain. In particular, we exploit word co-occurrences to capture relatedness of words in order to generate numeric representations of the words' contexts. We use the self-organizing map, a well-known neural network model with unsupervised learning function, to map the high-dimensional data onto a two-dimensional representation for convenient browsing. This intuitive view on the domain vocabulary supports the construction and enrichment of domain ontologies by making relevant concepts and their relations evident. We underline this approach with an example from the tourism domain.},
booktitle = {Proceedings of the First {Asian-Pacific} Conference on Conceptual Modelling : 2004, Dunedin, New Zealand},
publisher = {Australian computer society, Inc.},
author = {Michael Dittenbach and Helmut Berger and Dieter Merll},
year = {2004},
keywords = {Ontologie},
pages = {91--100},
annote = {{{\textless}p{\textgreater}dittenbachMichael2002.pdf{\textless}/p{\textgreater}}} },
-
H. S. Pinto and J. P. Martins, "Ontologies : how can they be built?," Knowledge and information systems, vol. 6, iss. 4, pp. 441-464, 2004.
@article{pinto_ontologies_2004, title = {Ontologies : how can they be built?},
volume = {6},
abstract = {Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.},
number = {4},
journal = {Knowledge and information systems},
author = {Helena Sofia Pinto and João P. Martins},
year = {2004},
keywords = {Méthodologie, Ontologie},
pages = {441--464},
annote = {{{\textless}p{\textgreater}pintoHelena2004.pdf{\textless}/p{\textgreater}}} },
-
C. Wen-Tao, W. Sheng-Rui, and J. Qing-Shan, "Address extraction: a graph matching and ontology-based approach to conceptual information retrieval," , Shanghai, China, 2004, pp. 1571-6.
@inproceedings{wen-tao_address_2004, address = {Shanghai, China},
series = {Proceedings of 2004 International Conference on Machine Learning and Cybernetics {(IEEE} Cat. {No.04EX826)}},
title = {Address extraction: a graph matching and ontology-based approach to conceptual information retrieval},
volume = {vol.3},
abstract = {Address and related location-awareness information can be retrieved and extracted from the Web using content-based {IR} technologies. Much recent research on content-based information retrieval focuses on conceptual analysis of unstructured texts on the Web. This paper illustrates an address extraction application to achieve an ontology-based conceptual {IR} system with graph matching. Our key idea is that a document can be represented as a sub-graph of a predefined ontology graph. An approximate graph matching approach is used for content (address) extraction. This work is part of an ongoing project to develop an intelligent search agent to support driving-related information extraction from the Web},
publisher = {{IEEE}},
author = {Cai {Wen-Tao} and Wang {Sheng-Rui} and Jiang {Qing-Shan}},
year = {2004},
note = {Copyright 2005, {IEE}},
keywords = {Extraction d'information, Intelligence artificielle, Ontologie},
pages = {1571--6},
annote = {{\textless}p{\textgreater}8262480 address extraction ontology graph matching conceptual information retrieval Web content based information retrieval intelligent search agent{\textless}/p{\textgreater}} },
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F. Rastier, "Ontologie(s)," Revue des sciences et technologies de l’information, vol. 18, iss. 1, pp. 15-40, 2004.
@article{rastier_ontologies_2004, series = {Revue {d’Intelligence} artificielle},
title = {Ontologie(s)},
volume = {18},
url = {http://www.revue-texto.net/1996-2007/Inedits/Rastier/Rastier_Ontologies.html},
abstract = {Les réseaux sémantiques que l’on construit à présent sont nommés ontologies. Au-delà de la logique, ce choix repose sur un lien réaffirmé avec la tradition métaphysique. Cependant, les lexiques des langues ne sont pas structurés comme des ontologies. Les relations sémantiques sont en effet plus complexes et variables que ce que prévoient les constructeurs d’ontologies. Pour construire une “ dé-ontologie ”, il faut restituer la diversité des discours et des genres, qui rendent illusoire une ontologie unique ; insister sur le problème de la diversité sémiotique des textes, les corrélations complexes entre contenu et expression, l’incidence constituante du contexte. C’est là une condition pour remplir des tâches de caractérisation, notamment en linguistique de corpus.},
number = {1},
journal = {Revue des sciences et technologies de l’information},
author = {François Rastier},
year = {2004},
keywords = {Ontologie},
pages = {15--40} },
-
D. Zhang and W. S. Lee, "Learning to integrate Web taxonomies," Web Semantics: Science, Services and Agents on the World Wide Web, vol. 2, iss. 2, pp. 131-151, 2004.
@article{zhang_learning_2004, title = {Learning to integrate Web taxonomies},
volume = {2},
url = {http://www.sciencedirect.com/science/article/B758F-4DS962C-1/2/8f2f5cedab10f505a7b404e03cc0788f},
abstract = {We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects from the source taxonomies into categories of the master taxonomy. However, conventional machine learning algorithms totally ignore the availability of the source taxonomies. In fact, source and master taxonomies often have common categories under different names or other more complex semantic overlaps. We introduce two techniques that exploit the semantic overlap between the source and master taxonomies to build better classifiers for the master taxonomy. The first technique, Cluster Shrinkage, biases the learning algorithm against splitting source categories by making objects in the same category appear more similar to each other. The second technique, {Co-Bootstrapping,} tries to facilitate the exploitation of inter-taxonomy relationships by providing category indicator functions as additional features for the objects. Our experiments with real-world Web data show that these proposed add-on techniques can enhance various machine learning algorithms to achieve substantial improvements in performance for taxonomy integration.},
number = {2},
journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
author = {Dell Zhang and Wee Sun Lee},
year = {2004},
keywords = {Apprentissage machine, Classification, Ontologie, Taxonomie, Web sémantique},
pages = {131--151} },
-
M. Denny, Ontology tools survey, revisited, 2004.
@misc{denny_ontology_2004, title = {Ontology tools survey, revisited},
url = {http://www.xml.com/2002/11/06/Ontology_Editor_Survey.html},
abstract = {A new survey of ontology editors was conducted as a follow-up to an initial survey conducted in 2002. The results of the survey are summarized in this article. The results of the original survey may be found at www.xml.com/pub/a/2002/11/06/ontologies.html.},
journal = {xml.com},
author = {Michael Denny},
year = {2004},
keywords = {Ontologie},
howpublished = {{http://www.xml.com/2002/11/06/Ontology\_Editor\_Survey.html}},
annote = {{{\textless}p{\textgreater}dennyMichael2004.pdf{\textless}/p{\textgreater}}} },
-
M. Shamsfard and A. A. Barforoush, "Learning ontologies from natural language texts," International journal of human-computer studies, vol. 60, iss. 1, pp. 17-63, 2004.
@article{shamsfard_learning_2004, title = {Learning ontologies from natural language texts},
volume = {60},
abstract = {Research on ontology is becoming increasingly widespread in the computer science community. The major problems in building ontologies are the bottleneck of knowledge acquisition and time-consuming construction of various ontologies for various domains/ applications. Meanwhile moving toward automation of ontology construction is a {solution.We} proposed an automatic ontology building approach. In this approach, the system starts from a small ontology kernel and constructs the ontology through text understanding automatically. The kernel contains the primitive concepts, relations and operators to build an ontology. The features of our proposed model are being domain/application independent, building ontologies upon a small primary kernel, learning words, concepts, taxonomic and non-taxonomic relations and axioms and applying a symbolic, hybrid ontology learning approach consisting of logical, linguistic based, template driven and semantic analysis {methods.Hasti} is an ongoing project to implement and test the automatic ontology building approach. It extracts lexical and ontological knowledge from Persian {(Farsi)} {texts.In} this paper, at first, we will describe some ontology engineering problems, which motivated our approach. In the next sections, after a brief description of Hasti, its features and its architecture, we will discuss its components in detail. In each part, the learning algorithms will be described. Then some experimental results will be discussed and at last, we will have an overview of related works and will introduce a general framework to compare ontology learning systems and will compare Hasti with related works according to the framework.},
number = {1},
journal = {International journal of human-computer studies},
author = {Mehrnoush Shamsfard and Ahmad Abdollahzadeh Barforoush},
year = {2004},
keywords = {Langage naturel, Ontologie},
pages = {17--63} },
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L. Stojanovic, J. Schneider, A. Maedche, S. Libischer, R. Studer, T. Lumpp, A. Abecker, G. Breiter, and J. Dinger, "The role of ontologies in autonomic computing systems," IBM Systems Journal, vol. 43, iss. 3, pp. 598-616, 2004.
@article{stojanovic_role_2004, series = {Unstructured Information Management},
title = {The role of ontologies in autonomic computing systems},
volume = {43},
url = {http://www.research.ibm.com/journal/sj/433/stojanovic.pdf},
doi = {10.1147/sj.433.0598},
abstract = {The goal of {IBM's} autonomic computing strategy is to deliver information technology environments with improved self-management capabilities, such as self-healing, self-protection, self-optimization, and self-configuration. Data correlation and inference technologies can be used as core components to build autonomic computing systems. They can also be used to perform automated and continuous analysis of enterprise-wide event data based upon user-defined configurable rules, such as those intended for detecting threats or system failures. Furthermore, they may trigger corrective actions for protecting or healing the system. In this paper, we discuss the use of ontologies as a high-level, expressive, conceptual modeling approach for describing the knowledge upon which the processing of a correlation engine is based. The introduction of explicit models of state-based information technology resources into the correlation technology approach allows the construction of autonomic computing systems that are capable of dealing with policy-based goals on a higher abstraction level. We demonstrate some of the benefits of this approach by applying it to a particular {IBM} implementation, the {eAutomation} correlation engine.},
number = {3},
journal = {{IBM} Systems Journal},
author = {Ljiljana Stojanovic and Juergen Schneider and Alexander Maedche and Susanne Libischer and Rudi Studer and Thomas Lumpp and Andreas Abecker and Gerd Breiter and John Dinger},
year = {2004},
keywords = {Ontologie},
pages = {598--616},
annote = {{{\textless}p{\textgreater}stojanovicLjiljana2004.pdf{\textless}/p{\textgreater}}} },
-
R. Mizoguchi, Essentials of ontological engineering, 2004.
@misc{mizoguchi_essentials_2004, title = {Essentials of ontological engineering},
author = {Riichiro Mizoguchi},
year = {2004},
keywords = {Ontologie} },
-
I. Jurisica, J. Mylopoulos, and E. Yu, "Ontologies for knowledge management : an information systems perspective," Knowledge and information systems, vol. 6, iss. 4, pp. 380-401, 2004.
@article{jurisica_ontologies_2004, title = {Ontologies for knowledge management : an information systems perspective},
volume = {6},
url = {http://www.springerlink.com/content/fm7htmlyyj7flnvn/fulltext.pdf},
doi = {10.1007/s10115-003-0135-4},
abstract = {Knowledge management research focuses on concepts, methods, and tools supporting the management of human knowledge. The main objective of this paper is to survey basic concepts that have been used in computer science for the representation of knowledge and summarize some of their advantages and drawbacks. A secondary objective is to relate these techniques to information science theory and practice. The survey classifies the concepts used for knowledge representation into four broad ontological categories. Static ontologies describe static aspects of the world, i.e., what things exist, their attributes and relationships. A dynamic ontology, on the other hand, describes the changing aspects of the world in terms of states, state transitions and processes. Intentional ontologies encompass the world of things agents believe in, want, prove or disprove, and argue about. Finally, social ontologies cover social settings – agents, positions, roles,
authority, permanent organizational structures or shifting networks of alliances and interdependencies.},
number = {4},
journal = {Knowledge and information systems},
author = {Igor Jurisica and John Mylopoulos and Eric Yu},
year = {2004},
keywords = {Gestion des connaissances, Ontologie},
pages = {380--401},
annote = {{{\textless}p{\textgreater}jurisicaIgor2004.pdf{\textless}/p{\textgreater}}} },
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H. Knublauch, M. A. Musen, and A. L. Rector, "Editing description logic ontologies with the protégé OWL plugin," in International Workshop on Description Logics, 2004.
@inproceedings{knublauch_editing_2004, series = {Graphical Interfaces 1},
title = {Editing description logic ontologies with the protégé {OWL} plugin},
volume = {104},
url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-104/08Knublauch-final.pdf},
abstract = {The growing interest in the Semantic Web and the Web Ontology Language {(OWL)} will reveal the potential of Description Logics in industrial projects. The rich semantics of {OWL} provide powerful reasoning capabilities that help build, maintain and query domain models for many purposes. However, before {OWL} can unfold its full potential, user-friendly tools with a scalable architecture are required. We present the {OWL} Plugin, an extension of the Protégé ontology development environment, which can be used to define classes and properties, to edit logical class expressions, to invoke reasoners, and to link ontologies into the Semantic Web. We analyze some of the challenges for developers of Description Logic editors, and discuss some of our user interface design decisions.},
booktitle = {International Workshop on Description Logics},
author = {Holger Knublauch and Mark A. Musen and Alan L. Rector},
year = {2004},
keywords = {Ontologie},
annote = {{{\textless}p{\textgreater}knublauchHolger2004.pdf{\textless}/p{\textgreater}}} },
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E. C. Mavrikas, N. Nicoloyannis, and E. Kavakli, "Cultural heritage information on the semantic Web," in Engineering knowledge in the age of the semantic Web : 14th international conference, EKAW 2004 : proceedings, 2004, pp. 477-8.
@inproceedings{mavrikas_cultural_2004, series = {Lecture notes in computer science; 3257. Lecture notes in artificial intelligence},
title = {Cultural heritage information on the semantic Web},
abstract = {In this paper, we outline an ontology-driven approach to the organisation, classification, and mining of cultural heritage documents on the semantic Web. We propose its implementation as a person-machine system that uses statistical {NLP} methods to extract cultural heritage information from texts contained in distributed information sources connected within a schema-based peer-to-peer network infrastructure},
booktitle = {Engineering knowledge in the age of the semantic Web : 14th international conference, {EKAW} 2004 : proceedings},
publisher = {{Springer-Verlag}},
author = {E. C. Mavrikas and N. Nicoloyannis and E. Kavakli},
year = {2004},
note = {Copyright 2005, {IEE}},
keywords = {Fouille de donnée, Langage naturel, Ontologie, Web sémantique},
pages = {477--8},
annote = {{\textless}p{\textgreater}8303562 semantic Web ontology-driven approach cultural heritage document mining person-machine system statistical natural language processing methods distributed information sources peer-to-peer network{\textless}/p{\textgreater}} },
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R. Navigli and P. Velardi, "Learning domain ontologies from document warehouses and dedicated web sites," Comput. Linguist., vol. 30, iss. 2, pp. 151-179, 2004.
@article{navigli_learning_2004-1, title = {Learning domain ontologies from document warehouses and dedicated web sites},
volume = {30},
issn = {0891-2017},
url = {http://portal.acm.org/ft_gateway.cfm?id=1105712&type=pdf&coll=GUIDE&dl=GUIDE,&CFID=4990800&CFTOKEN=85692913},
doi = {10.1162/089120104323093276},
abstract = {We present a method and a tool, {OntoLearn,} aimed at the extraction of domain ontologies from Web sites, and more generally from documents shared among the members of virtual organizations. {OntoLearn} first extracts a domain terminology from available documents. Then, complex domain terms are semantically interpreted and arranged in a hierarchical fashion. Finally, a general-purpose ontology, {WordNet,} is trimmed and enriched with the detected domain concepts. The major novel aspect of this approach is semantic interpretation, that is, the association of a complex concept with a complex term . This involves finding the appropriate {WordNet} concept for each word of a terminological string and the appropriate conceptual relations that hold among the concept components. Semantic interpretation is based on a new word sense disambiguation algorithm, called structural semantic interconnections.},
number = {2},
journal = {Comput. Linguist.},
author = {Roberto Navigli and Paola Velardi},
year = {2004},
keywords = {Ontologie, Web},
pages = {151--179},
annote = {{{\textless}p{\textgreater}navigliRoberto2004\_1.pdf{\textless}/p{\textgreater}}} },
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P. Cimiano, A. Hotho, G. Stumme, and J. Tane, "Conceptual knowledge processing with formal concept analysis and ontologies." 2961: , 2004, pp. 189-207.
@incollection{cimiano_conceptual_2004, address = {2961},
series = {Lecture Notes in Artificial Intelligence},
title = {Conceptual knowledge processing with formal concept analysis and ontologies},
isbn = {0302-9743},
abstract = {Among many other knowledge representations formalisms, Ontologies and Formal Concept Analysis {(FCA)} aim at modeling 'concepts'. We discuss how these two formalisms may complement another from an application point of view. In particular, we will see how {FCA} can be used to support Ontology Engineering, and how ontologies can be exploited in {FCA} applications. The interplay of {FCA} and ontologies is studied along the life cycle of an ontology: (i) {FCA} can support the building of the ontology as a learning technique. (ii) The established ontology can be analyzed and navigated by using techniques of {FCA.} (iii) Last but not least, the ontology may be used to improve an {FCA} application.},
booktitle = {Concept Lattices, Proceedings},
author = {P. Cimiano and A. Hotho and G. Stumme and J. Tane},
year = {2004},
keywords = {Ontologie},
pages = {189--207} },
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A. Gilchrist, "Thesauri, taxonomies and ontologies : an etymological note," Journal of Documentation, vol. 59, iss. 1, pp. 7-18, 2003.
@article{gilchrist_thesauri_2003, title = {Thesauri, taxonomies and ontologies : an etymological note},
volume = {59},
url = {http://thesius.emeraldinsight.com/10.1108/00220410310457984},
abstract = {The amount of work to be done in rendering the digital information space more efficient and effective has attracted a wide range of disciplines which, in turn, has given rise to a degree of confusion in the terminology applied to information problems. This note seeks to shed some light on the three terms thesauri, taxonomies and ontologies as they are currently being used by, among others, information scientists, {AI} practitioners, and those working on the foundations of the semantic Web. The paper is not a review of the techniques themselves.},
number = {1},
journal = {Journal of Documentation},
author = {Alan Gilchrist},
year = {2003},
keywords = {Ontologie, Taxonomie, Thésaurus},
pages = {7 -- 18},
annote = {{{\textless}p{\textgreater}alanGilchrist2002.pdf{\textless}/p{\textgreater}}} },
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D. Bourigault and N. Aussenac-Gilles, "Construction d’ontologies à partir de textes," in Traitement automatique des langues natureles (TALN), 2003.
@inproceedings{bourigault_construction_2003, title = {Construction d'ontologies à partir de textes},
abstract = {Cet article constitue le support d'un cours présenté lors de la conférence {TALN} 2003. Il défend la place du Traitement Automatique des Langues comme discipline clé pour le développement de ressources termino-ontologiques à partir de textes. Les contraintes et enjeux de ce processus sont identifiés, en soulignant l'importance de considérer cette tâche comme un processus supervisé par un analyste. Sont présentés un certain nombre d'outils logiciels et méthodologiques venant de plusieurs disciplines comme le {TAL} et l'ingénierie des connaissances qui peuvent aider l'analyste dans sa tâche. Divers retours d'expérience sont présentés. This paper gathers the notes of a tutorial. We advocate in favour of the role of Natural Language Processing as a key discipline for the development of terminological and ontological resources from texts. The constraints and challenges of this process are identified, and lead to underline this task as a supervised processes carried out by an analyst. We present several software and methodological tools from {NLP} and knowledge engineering that can be use for to assist the analyst. Our suggestion rely on various experience feed-back.},
booktitle = {Traitement automatique des langues natureles {(TALN)}},
author = {Didier Bourigault and Nathalie {Aussenac-Gilles}},
month = jun, year = {2003},
keywords = {Fouille de texte, Ontologie},
annote = {{{\textless}p{\textgreater}bourigaultDidier2003.pdf{\textless}/p{\textgreater}}} },
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R. Mizoguchi, "Introduction to ontological engineering," New generation computing, vol. 21, iss. 4, pp. 365-384, 2003.
@article{mizoguchi_introduction_2003, series = {Ontological engineering / Ontology engineering : tutorial},
title = {Introduction to ontological engineering},
volume = {21},
url = {http://www.ei.sanken.osaka-u.ac.jp/pub/miz/Part1-pdf2.pdf},
abstract = {This tutorial course describes the current state of the art of ontological engineering which is a successor of knowledge engineering. It covers theory, tools and applications and consists of three parts: Part 1 is an introduction to ontological engineering, Part 2 describes ontology development, languages and tools, and Part 3 is an advanced course dealing with philosophical issues of ontology design together with detailed guidelines of ontology development. Part 3 also presents a success story of ontological engineering with the deployment result in a company. The philosophy behind this tutorial is that ontological engineering is viewed as a challenge to enabling knowledge sharing and reuse which knowledge engineering failed to realize. Therefore, one of the major topics dealt with in this tutorial is to explain what an ontology should be while explaining how it is understood currently.},
number = {4},
journal = {New generation computing},
author = {Riichiro Mizoguchi},
year = {2003},
keywords = {Ontologie},
pages = {365--384},
annote = {{{\textless}p{\textgreater}mizoguchiRiichiro2003.pdf{\textless}/p{\textgreater}}} },
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L. Schneider, "How to build a foundational ontology." Berlin; Heidelberg: Springer, 2003, pp. 120-134.
@incollection{schneider_to_2003, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 2821},
title = {How to build a foundational ontology},
isbn = {978-3-540-20059-8},
url = {http://www.springerlink.com/content/d3laqtvc5m7vb42g/},
abstract = {Foundational ontologies are axiomatic accounts of high-level domain-independent categories about the real world. They constitute toolboxes of reusable information modeling primitives for building application ontologies in specific domains. As such, they enhance semantic interoperability between agents by specifying descriptively adequate shared conceptualisations. The design of foundational ontologies gives rise to completely new challenges in respect of their content as well as their formalisation. Indeed, their underlying modeling options correspond to the ontological choices discussed in classical metaphysics as well as in the research on qualitative reasoning. Building a foundational ontology is thus an eminently interdisciplinary task. As a case study, this article sketches the formalisation of the {Object-Centered} High-level {REference} ontology {OCHRE,} emphasising in particular the problem of achieving formal simplicity within the limits of descriptive adequacy.},
booktitle = {{KI} 2003 : advances in artificial intelligence},
publisher = {Springer},
author = {L. Schneider},
year = {2003},
keywords = {Ontologie},
pages = {120--134},
annote = {{{\textless}p{\textgreater}schneiderL2003.pdf{\textless}/p{\textgreater}}} },
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H. Alani, S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis, and N. R. Shadbolt, "Automatic ontology-based knowledge extraction and Tailored biography generation from Web documents," IEEE Intelligent Systems, vol. 18, iss. 1, pp. 14-21, 2003.
@article{alani_automatic_2003, title = {Automatic ontology-based knowledge extraction and Tailored biography generation from Web documents},
volume = {18},
issn = {15411672},
doi = {10.1.1.57.9194},
abstract = {This paper presents recent developments in the Artequakt project which seeks to automatically extract knowledge about artists from the Web, populate a knowledge base, and use it to generate personalized narrative biographies. An overview of the system architecture is presented and the three key components of that architecture are explained in detail, namely knowledge extraction, information management and biography construction. An example experiment is detailed and further challenges are outlined.},
number = {1},
journal = {{IEEE} Intelligent Systems},
author = {Harith Alani and Sanghee Kim and David E Millard and Mark J Weal and Wendy Hall and Paul H Lewis and Nigel R Shadbolt},
year = {2003},
keywords = {Extraction d'information, Ontologie},
pages = {14--21},
annote = {{{\textless}p{\textgreater}alaniHarith2003.pdf{\textless}/p{\textgreater}}} },
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M. Missikoff, P. Velardi, and P. Fabriani, "Text mining techniques to automatically enrich a domain ontology," Applied Intelligence, vol. 18, iss. 3, pp. 323-340, 2003.
@article{missikoff_text_2003, title = {Text mining techniques to automatically enrich a domain ontology},
volume = {18},
issn = {{0924669X} {(Print)} 15737497 {(Online)}},
url = {http://www.springerlink.com/content/m717014453174720/fulltext.pdf},
doi = {10.1023/A:1023254205945},
abstract = {Though the utility of domain ontologies is now widely acknowledged in the {IT} {(Information} Technology) community, several barriers must be overcome before ontologies become practical and useful tools. A critical issue is the ontology construction, i.e., the task of identifying, defining, and entering the concept definitions. In case of large and complex application domains this task can be lengthy, costly, and controversial (since different persons may have different points of view about the same concept). To reduce time, cost (and, sometimes, harsh discussions) it is highly advisable to refer, in constructing or updating an ontology, to the documents available in the field. Text mining tools may be of great help in this task. The work presented in this paper illustrates the guidelines of {SymOntos,} ontology management system, and the text mining approach adopted herein to support ontology building. The latter operates by extracting, from the related literature, the prominent domain concepts and the semantic relations among them.},
number = {3},
journal = {Applied Intelligence},
author = {Michele Missikoff and Paola Velardi and Paolo Fabriani},
year = {2003},
keywords = {Fouille de texte, Langage naturel, Ontologie},
pages = {323--340},
annote = {{{\textless}p{\textgreater}missikovMichele2003.pdf{\textless}/p{\textgreater}}} },
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G. Jiang, K. Ogasawara, A. Endoh, and T. Sakurai, "Context-based ontology building support in clinical domains using formal concept analysis," International Journal of Medical Informatics, vol. 71, iss. 1, pp. 71-81, 2003.
@article{jiang_context-based_2003, title = {Context-based ontology building support in clinical domains using formal concept analysis},
volume = {71},
abstract = {Objective: Ontology in clinical domains is becoming a core research field in the realm of medical informatics. The objective of this study is to explore the potential role of formal concept analysis {(FCA)} in a context-based ontology building support in a clinical domain (e.g. cardiovascular medicine here). Methodology: We developed an ontology building support system that integrated an {FCA} module with a natural language processing {(NLP)} module. The user interface of the system was developed as a Protégé-2000 {JAVA} tab plug-in. A collection of 368 textual discharge summaries and a standard dictionary of Japanese diagnostic terms {(MEDIS} ver2.0) were used as the main knowledge sources. A preliminary evaluation was taken to show the usefulness of the system. Results: Stability was shown on the {MEDIS-based} medical concept extraction with high precision. 73±14\% {(mean±S.D.)} of the compound medical phrases extracted were sufficiently meaningful to form a medical concept from a clinical perspective. Also, 57.7\% of attribute implication pairs (i.e. medical concept pairs) extracted were identified as positive from a clinical perspective. Conclusion: Under the framework of our ontology building support system using {FCA,} the clinical experts could reach a mass of both linguistic information and context-based knowledge that was demonstrated as useful to support their ontology building tasks.},
number = {1},
journal = {International Journal of Medical Informatics},
author = {Guoqian Jiang and Katsuhiko Ogasawara and Akira Endoh and Tsunetaro Sakurai},
year = {2003},
keywords = {Ontologie, Recherche d'information},
pages = {71--81} },
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N. Sanguk, S. Haesung, C. Jaehyuk, C. Kyunghee, and J. Gihyun, "Classifying Web pages using adaptive ontology," , Washington, DC, USA, 2003, pp. 2144-9.
@inproceedings{sanguk_classifying_2003, address = {Washington, {DC,} {USA}},
series = {{SMC'03} Conference Proceedings. 2003 {IEEE} International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance {(Cat.} {No.03CH37483)}},
title = {Classifying Web pages using adaptive ontology},
volume = {vol.3},
url = {http://dx.doi.org/10.1109/ICSMC.2003.1244201},
abstract = {In this paper, we present an automated Web page classifier based on adaptive ontology. As a first step, to identify the representative terms given a set of classes, we compute the product of term frequency and document frequency. Secondly, the information gain of each term prioritizes it based on the possibility of classification. We compile the selected terms and classification into rules using machine learning algorithms. The compiled rules classify any Web page into categories defined on a domain ontology. In the experiments, 11 terms out of 1,700 terms were identified as representative features given a set of Web pages. The resulting accuracy of the classification was, on the average, 95.2\%},
publisher = {{IEEE}},
author = {Noh Sanguk and Seo Haesung and Choi Jaehyuk and Choi Kyunghee and Jung Gihyun},
year = {2003},
note = {Copyright 2004, {IEE}},
keywords = {Classification, Ontologie, Recherche d'information, Web},
pages = {2144--9},
annote = {{\textless}p{\textgreater}7953406 Web page classifier adaptive ontology term frequency document frequency information gain machine learning algorithms representative features{\textless}/p{\textgreater}} },
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R. Prieto-Diaz, "A faceted approach to building ontologies," , Las Vegas, NV, USA, 2003, pp. 458-65.
@inproceedings{prieto-diaz_faceted_2003, address = {Las Vegas, {NV,} {USA}},
series = {Proceedings of the 2003 {IEEE} International Conference on Information Reuse and Integration {(IRI} - 2003) {(IEEE} Cat. {No.03EX781)}},
title = {A faceted approach to building ontologies},
url = {http://dx.doi.org/10.1109/IRI.2003.1251451},
abstract = {An ontology can be defined as a conceptualization of a domain or subject area typically captured in an abstract model of how people think about things in the domain. Humans have been producing ontologies for millennia to understand and explain our rationale and environment. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. This paper describes how a domain analysis method based on faceted classification can be used for building ontologies. It relates domain analysis and ontologies, illustrates a step in the domain analysis method for identifying and categorizing concepts, and describes how this step, borrowed from library science, is incorporated into the domain analysis method. The paper also gives an overview of the method and describes a tool for automating parts of the process},
publisher = {{IEEE}},
author = {R. {Prieto-Diaz}},
year = {2003},
note = {Copyright 2004, {IEE}},
keywords = {Ontologie},
pages = {458--65},
annote = {{\textless}p{\textgreater}7862071 ontology building faceted classification domain analysis ontology capture ontology coding ontology integration vocabulary taxonomy library science text analysis conceptual clustering formal logic domain conceptualization domain understanding{\textless}/p{\textgreater}} },
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H. Alani, K. Sanghee, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis, and N. R. Shadbolt, "Automatic ontology-based knowledge extraction from Web documents," IEEE Intelligent Systems, vol. 18, iss. 1, pp. 14-21, 2003.
@article{alani_automatic_2003-1, title = {Automatic ontology-based knowledge extraction from Web documents},
volume = {18},
url = {http://dx.doi.org/10.1109/MIS.2003.1179189},
abstract = {To bring the Semantic Web to life and provide advanced knowledge services, we need efficient ways to access and extract knowledge from Web documents. Although Web page annotations could facilitate such knowledge gathering, annotations are rare and will probably never be rich or detailed enough to cover all the knowledge these documents contain. Manual annotation is impractical and unscalable, and automatic annotation tools remain largely undeveloped. Specialized knowledge services therefore require tools that can search and extract specific knowledge directly from unstructured text on the Web, guided by an ontology that details what type of knowledge to harvest. An ontology uses concepts and relations to classify domain knowledge. Other researchers have used ontologies to support knowledge extraction, but few have explored their full potential in this domain. The paper considers the Artequakt project which links a knowledge extraction tool with an ontology to achieve continuous knowledge support and guide information extraction. The extraction tool searches online documents and extracts knowledge that matches the given classification structure. It provides this knowledge in a machine-readable format that will be automatically maintained in a knowledge base {(KB).} Knowledge extraction is further enhanced using a lexicon-based term expansion mechanism that provides extended ontology terminology},
number = {1},
journal = {{IEEE} Intelligent Systems},
author = {H. Alani and Kim Sanghee and D. E. Millard and M. J. Weal and W. Hall and P. H. Lewis and N. R. Shadbolt},
year = {2003},
keywords = {Classification, Ontologie, Recherche d'information},
pages = {14--21},
annote = {{{\textless}p{\textgreater}Copyright} 2003, {IEE} 7556990 1094-7167 automatic ontology-based knowledge extraction Web documents Semantic Web knowledge access Web page annotations unstructured text domain knowledge classification Artequakt project online documents machine-readable format knowledge base lexicon-based term expansion terminology natural language processing{\textless}/p{\textgreater}} },
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I. Johansson, "Performatives and antiperformatives," Linguistics and Philosophy, vol. 26, iss. 6, pp. 661-702, 2003.
@article{johansson_performatives_2003, title = {Performatives and antiperformatives},
volume = {26},
issn = {0165-0157},
abstract = {The paper highlights a certain kind of self-falsifying utterance, which I shall call antiperformative assertions, not noted in speech-act theory thus far. By taking such assertions into account, the old question whether explicit performatives have a truth-value can be resolved. I shall show that explicit performatives are in fact self-verifyingly true, but they are not related to propositions the way ordinary assertions are; antiperformatives have the same unusual relation to propositions, but are self-falsifyingly false. Explicit performatives are speech acts performed in situations where it is important that the speaker is self-reflectively aware of what he is doing in the speech act. Antiperformatives, on the other hand, are speech acts performed in situations where lack of direct self-reflectiveness is required. In order to situate performatives and antiperformatives, the analysis is embedded within a more general discussion of self-falsifying and self-verifying assertions.},
number = {6},
journal = {Linguistics and Philosophy},
author = {Ingvar Johansson},
year = {2003},
keywords = {Linguistique, Philosophie},
pages = {661--702},
annote = {{{\textless}p{\textgreater}johanssonIngvar2003.pdf{\textless}/p{\textgreater}}} },
-
L. W. Kwong and Y. K. Ng, "Performing binary-categorization on multiple-record Web documents using information retrieval models and application ontologies," World Wide Web, vol. 6, pp. 281-303, 2003.
@article{kwong_performing_2003, title = {Performing binary-categorization on multiple-record Web documents using information retrieval models and application ontologies},
volume = {6},
url = {http://www.ingentaconnect.com/content/klu/wwwj/2003/00000006/00000003/05139850},
abstract = {To retrieve Web documents of interest, most of the Web users rely on Web search engines. All existing search engines provide query facility for users to search for the desired documents using search-engine keywords. However, when a search engine retrieves a long list of Web documents, the user might need to browse through each retrieved document in order to determine which document is of interest. We observe that there are two kinds of problems involved in the retrieval of Web documents: (1) an inappropriate selection of keywords specified by the user; and (2) poor precision in the retrieved Web documents. In solving these problems, we propose an automatic binary-categorization method that is applicable for recognizing multiple-record Web documents of interest, which appear often in advertisement Web pages. Our categorization method uses application ontologies and is based on two information retrieval models, the Vector Space Model {(VSM)} and the Clustering Model {(CM).} We analyze and cull Web documents to just those applicable to a particular application ontology. The culling analysis (i) uses {CM} to find a virtual centroid for the records in a Web document, (ii) computes a vector in a multi-dimensional space for this centroid, and (iii) compares the vector with the predefined ontology vector of the same multi-dimensional space using {VSM,} which we consider the magnitudes of the vectors, as well as the angle between them. Our experimental results show that we have achieved an average of 90\% recall and 97\% precision in recognizing Web documents belonged to the same category (i.e., domain of interest). Thus our categorization discards very few documents it should have kept and keeps very few it should have discarded.},
journal = {World Wide Web},
author = {L. W. Kwong and Y. K. Ng},
year = {2003},
keywords = {Catégorisation, Cluster, Ontologie},
pages = {281--303} },
-
A. Hotho, S. Staab, and G. Stumme, "Ontologies improve text document clustering," in Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, 2003, pp. 541-544.
@inproceedings{hotho_ontologies_2003, title = {Ontologies improve text document clustering},
isbn = {0769519784},
abstract = {Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not cooccur literally. In order to deal with the problem, we integrate core ontologies as background knowledge into the process of clustering text documents. Our experimental evaluations compare clustering techniques based on pre-categorizations of texts from Reuters newsfeeds and on a smaller domain of an {eLearning} course about Java. In the experiments, improvements of results by background knowledge compared to a baseline without background knowledge can be shown in many interesting combinations.},
booktitle = {Data Mining, 2003. {ICDM} 2003. Third {IEEE} International Conference on},
author = {Andreas Hotho and Steffen Staab and Gerb Stumme},
year = {2003},
keywords = {Cluster, Ontologie},
pages = {541--544},
annote = {{{\textless}p{\textgreater}hothoAndreas2003.pdf{\textless}/p{\textgreater}}} },
-
G. Stumme, "Off to new shores : conceptual knowledge discovery and processing," International Journal of Human-Computer Studies, vol. 59, iss. 3, pp. 287-325, 2003.
@article{stumme_off_2003, title = {Off to new shores : conceptual knowledge discovery and processing},
volume = {59},
url = {http://www.sciencedirect.com/science/article/B6WGR-48M7V77-1/2/3e2aeca1e3bf2240d301a815012fe2ed},
abstract = {In the last years, the main orientation of formal concept analysis {(FCA)} has turned from mathematics towards computer science. This article provides a review of this new orientation and analyses why and how {FCA} and computer science attracted each other. It discusses {FCA} as a knowledge representation formalism using five knowledge representation principles provided by Davis et al. (1993). It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas {FCA} and ontology engineering. The second part of the article discusses three lines of research which witness the new orientation of {FCA:} {FCA} as a conceptual clustering technique and its application for supporting the merging of ontologies; the efficient computation of association rules and the structuring of the results; and the visualization and management of conceptual hierarchies and ontologies including its application in an email management system.},
number = {3},
journal = {International Journal of {Human-Computer} Studies},
author = {Gerd Stumme},
year = {2003},
keywords = {Découverte de connaissances, Ontologie},
pages = {287--325} },
-
B. Hardy-Vallée, "Quand penser c’est faire : les concepts devenus naturels," PhD Thesis , 2003.
@phdthesis{hardy-valle_quand_2003, type = {Mémoire de maîtrise en philosophie},
title = {Quand penser c'est faire : les concepts devenus naturels},
school = {Université du Québec à Montréal},
author = {Benoit {Hardy-Vallée}},
month = oct, year = {2003},
keywords = {Philosophie},
pages = {186 p.},
annote = {{{\textless}p{\textgreater}hardy-valleeBenoit2003.pdf{\textless}/p{\textgreater}}} },
-
A. Hotho, S. Staab, and G. Stumme, "Explaining text clustering results using semantic structures." 2003.
@inproceedings{hotho_explaining_2003, title = {Explaining text clustering results using semantic structures},
url = {http://citeseer.ist.psu.edu/article/hotho03explaining.html},
abstract = {Common text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.},
author = {A. Hotho and S. Staab and G. Stumme},
year = {2003},
keywords = {Ontologie} },
-
R. Adaikkalavan, L. Elkhalifa, and A. Y. Aslandogan, "Topic identification through ontology-based concept generalization," University of Texas in Arlington, Texas, États-Unis, Project report CSE-2003-26, 2003.
@techreport{adaikkalavan_topic_2003, address = {Texas, {États-Unis}},
type = {Project report},
title = {Topic identification through ontology-based concept generalization},
url = {http://www.cse.uta.edu/Research/Publications/Downloads/CSE-2003-26.pdf},
abstract = {We present a method for topic identification of web pages based on contextual support and structural term weighting. For topic selection, concept expansion is performed through an ontology such as the {WordNet.} The experimental evaluation suggests that the approach is promising and can be adapted to many categorization tasks.},
number = {{CSE-2003-26}},
institution = {University of Texas in Arlington},
author = {Raman Adaikkalavan and Laali Elkhalifa and Y. Alp Aslandogan},
year = {2003},
keywords = {Analyse de contenu, Ontologie},
annote = {{{\textless}p{\textgreater}adaikkalavanRaman2003.pdf{\textless}/p{\textgreater}}} },
-
O. Corcho, M. Fernandez-Lopez, and A. Gomez-Perez, "Methodologies, tools and languages for building ontologies. Where is their meeting point?," Data \& Knowledge Engineering, vol. 46, iss. 1, pp. 41-64, 2003.
@article{corcho_methodologies_2003, title = {Methodologies, tools and languages for building ontologies. Where is their meeting point?},
volume = {46},
issn = {{0169-023X}},
abstract = {In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available. {(C)} 2002 Elsevier Science {B.V.} All rights reserved.},
number = {1},
journal = {Data \& Knowledge Engineering},
author = {O. Corcho and M. {Fernandez-Lopez} and A. {Gomez-Perez}},
year = {2003},
keywords = {Méthodologie, Ontologie},
pages = {41--64} },
-
J. H. Chiang, "MeKE : discovering the functions of gene products from biomedical literature via sentence alignment," Bioinformatics, vol. 19, iss. 11, pp. 1417-1422, 2003.
@article{chiang_meke_2003, title = {{MeKE} : discovering the functions of gene products from biomedical literature via sentence alignment},
volume = {19},
abstract = {Motivation: Research on roles of gene products in cells is accumulating and changing rapidly, but most of the results are still reported in text form and are not directly accessible by computers. To expedite the progress of functional bioinformatics, it is, therefore, important to efficiently process large amounts of biomedical literature and transform the knowledge extracted into a structured format usable by biologists and medical researchers. Our aim was to develop an intelligent text-mining system that will extract from biomedical documents knowledge about the functions of gene products and thus facilitate computing with function. Results: We have developed an ontology-based text-mining system to efficiently extract from biomedical literature knowledge about the functions of gene products. We also propose methods of sentence alignment and sentence classification to discover the functions of gene products discussed in digital texts. Availability: http://ismp.csie.ncku.edu.tw/{\textasciitilde}yuhc/meke/ Contact: jchiang@mail.ncku.edu.tw},
number = {11},
journal = {Bioinformatics},
author = {J. H. Chiang},
year = {2003},
keywords = {Ontologie},
pages = {1417--1422} },
-
C. W. Chan, "Cognitive modeling and representation of knowledge in ontological engineering," Brain and Mind, vol. 4, iss. 2, pp. 269-282, 2003.
@article{chan_cognitive_2003, title = {Cognitive modeling and representation of knowledge in ontological engineering},
volume = {4},
abstract = {This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence {(AI)} has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools.},
number = {2},
journal = {Brain and Mind},
author = {C. W. Chan},
year = {2003},
keywords = {Ontologie, Science cognitive},
pages = {269--282},
annote = {{\textless}p{\textgreater}processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction = Bien définir ces processus avant de se lancer dans l\'élaboration d\'une méthodologie{\textless}/p{\textgreater}} },
-
C. Gieger, H. Deneke, and J. Fluck, "The future of text mining in genome-based clinical research," Biosilico, vol. 1, iss. 3, pp. 97-102, 2003.
@article{gieger_future_2003, title = {The future of text mining in genome-based clinical research},
volume = {1},
url = {http://www.sciencedirect.com/science/article/B75GS-4BNT1YB-6/2/7e5d406bacd2769e5b2a33be958adb5f},
abstract = {Efficient information retrieval and extraction is a major challenge in molecular biology and genome-based clinical research. In addition, there is an increasing demand to combine information from different resources and across different disciplines in life sciences. Unfortunately, a large proportion of this information is only available in scientific articles. Moreover, the volume of literature is growing almost exponentially. Text mining provides methods to retrieve and extract information contained in free-text automatically. Here, we discuss the challenges and limitations of text mining in biology and medicine, including unsolved problems and necessary developments.},
number = {3},
journal = {Biosilico},
author = {Christian Gieger and Hartwig Deneke and Juliane Fluck},
year = {2003},
keywords = {Extraction d'information, Fouille de texte, Ontologie, Recherche d'information},
pages = {97--102} },
-
Shui-Lung and Lee-Feng, "Enriching Web taxonomies through subject categorization of query terms from search engine logs," Decision Support Systems, vol. 35, iss. 1, pp. 113-127, 2003.
@article{chuang_enriching_2003, title = {Enriching Web taxonomies through subject categorization of query terms from search engine logs},
volume = {35},
issn = {01679236},
url = {http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6V8S-45XTR57-2-P&_cdi=5878&_user=789722&_orig=na&_coverDate=04%2F30%2F2003&_sk=999649998&view=c&wchp=dGLbVtz-zSkWz&md5=a6fb5d8cf61e1fde08f705cf2df7c1bd&ie=/sdarticle.pdf},
abstract = {In this paper, we propose a query-categorization approach to facilitating the engineering process of constructing Web taxonomies. One primary step in taxonomy construction is to acquire the domain-specific terminology terms and the mapping between the subjects and these terms. We introduce a technique for categorizing Web query terms from the logs of on-line search services into a predefined subject taxonomy based on their supposed popular search interests. The obtained experimental results show our technique's effectiveness in reducing the workload of human indexers in constructing Web taxonomies and also show its usefulness in various Web information retrieval applications.},
number = {1},
journal = {Decision Support Systems},
author = {{Shui-Lung} Chuang and {Lee-Feng} Chien},
year = {2003},
keywords = {Catégorisation, Ontologie, Recherche d'information, Web},
pages = {113--127},
annote = {{{\textless}p{\textgreater}chuangShui-lung2003.pdf{\textless}/p{\textgreater}}} },
-
F. J. Pelletier and R. H. Thomason, "Twenty-five years of linguistics and philosophy," Linguistics and philosophy, vol. 25, iss. 5, pp. 507-529, 2002.
@article{pelletier_twenty-five_2002, title = {Twenty-five years of linguistics and philosophy},
volume = {25},
issn = {0165-0157 {(Print)} 1573-0549 {(Online)}},
doi = {10.1023/A:1020885317387},
number = {5},
journal = {Linguistics and philosophy},
author = {Francis Jeffry Pelletier and Richmond H. Thomason},
month = dec, year = {2002},
keywords = {Linguistique, Philosophie},
pages = {507--529},
annote = {{{\textless}p{\textgreater}pelletierFrancis2002.pdf{\textless}/p{\textgreater}}} },
-
S. Loiseau, "Sémantique du discours philosophique : Deleuze commentateur de Spinoza," PhD Thesis , 2002.
@phdthesis{loiseau_smantique_2002, type = {Mémoire de {DEA} en sciences du langage},
title = {Sémantique du discours philosophique : Deleuze commentateur de Spinoza},
school = {Université Paris X Nanterre},
author = {Sylvain Loiseau},
month = sep, year = {2002},
keywords = {Philosophie},
pages = {83 p.},
annote = {{{\textless}p{\textgreater}loiseauSylvain2002.pdf{\textless}/p{\textgreater}}} },
-
M. Fernandez-Lopez and A. Gomez-Perez, "Overview and analysis of methodologies for building ontologies," Knowledge Engineering Review, vol. 17, iss. 2, pp. 129-156, 2002.
@article{fernandez-lopez_overview_2002, title = {Overview and analysis of methodologies for building ontologies},
volume = {17},
issn = {0269-8889},
abstract = {The use of methodologies in software and knowledge engineering is very extensive due to their important advantages. In the case of the development of ontologies, until now, several methodological proposals have been presented for building ontologies. Some of these methodologies are designed for building ontologies from scratch or reusing other ontologies without modifying them, concretely, the following cases can be mentioned: the Cyc methodology, the approach proposed by Uschold and King, Gruninger and Fox's methodology, the {KACTUS} methodology, {METHONTOLOGY} and the {SENSUS} methodology. There is even a proposal for re-engineering ontologies, and several proposals for collaborative construction of ontologies. In this article, we describe the methodologies and check their degree of maturity, contrasting them with respect to the {IEEE} standard for software development. Before this, we justify to what extent this standard can be used. A conclusion to this study is that there is no completely mature methodological proposal for building ontologies, since there are some important activities and techniques that are missing in all these methodologies. However, all the methodologies do not have the same degree of maturity. In fact, {METHONTOLOGY} is a very mature methodology. The other conclusion of this article is that, although work to unify proposals can be interesting, maybe several approaches should coexist.},
number = {2},
journal = {Knowledge Engineering Review},
author = {M. {Fernandez-Lopez} and A. {Gomez-Perez}},
year = {2002},
keywords = {Méthodologie, Ontologie},
pages = {129--156} },
-
D. Forest, "Lecture et analyse de textes philosophiques assistées par ordinateur : application d’une approche classificatoire mathématique à l’analyse thématique du « Discours de la méthode » et des « Méditations métaphysiques » de Descartes," PhD Thesis , 2002.
@phdthesis{forest_lecture_2002, title = {Lecture et analyse de textes philosophiques assistées par ordinateur : application d'une approche classificatoire mathématique à l'analyse thématique du « Discours de la méthode » et des « Méditations métaphysiques » de Descartes},
school = {Université du Québec à Montréal, Philosophie},
author = {Dominic Forest},
year = {2002},
note = {Maîtrise en philosophie},
keywords = {Analyse de texte, Fouille de texte, Philosophie},
pages = {131} },
-
B. Hardy-Vallée, "Quand penser c’est faire : les concepts devenus naturels," PhD Thesis , 2002.
@phdthesis{hardy-valle_quand_2002, type = {Mémoire de maîtrise en philosophie},
title = {Quand penser c'est faire : les concepts devenus naturels},
school = {Université du Québec à Montréal},
author = {Benoit {Hardy-Vallée}},
month = sep, year = {2002},
keywords = {Philosophie},
pages = {201 p.},
annote = {{{\textless}p{\textgreater}hardy-valleeBenoit2002.pdf{\textless}/p{\textgreater}}} },
-
M. Degeratu and V. Hatzivassiloglou, "Building automatically a business registration ontology," , Los Angeles, California, 2002, pp. 1-7.
@inproceedings{degeratu_building_2002, address = {Los Angeles, California},
series = {{ACM} International Conference Proceeding Series},
title = {Building automatically a business registration ontology},
volume = {129},
url = {http://portal.acm.org/citation.cfm?id=1123108&coll=portal&dl=ACM&CFID=31737443&CFTOKEN=41461128},
abstract = {We discuss a domain-independent, corpus based method for dictionary-less automatic extraction of ontological knowledge from domain-specific unannotated documents. We present the architecture, algorithms, and results for {ONTOSTRUCT---a} new system that uses machine learning and statistical techniques to analyze text sources, discover terms, link equivalent terms into concepts, learn both hierarchical and non-hierarchical conceptual relations, and build an extensive, semantically sound hierarchy of concepts. We report on {ONTOSTRUCT's} results in constructing a domain-specific ontology for the business registration domain, and evaluate the performance of two of its modules.},
author = {Melania Degeratu and Vasileios Hatzivassiloglou},
year = {2002},
keywords = {Ontologie},
pages = {1 -- 7} },
-
D. Sogbohossou, "Ontologies, vocabulaires XML, représentation et exploitation des connaissances dans le cadre d’une organisation," PhD Thesis , 2002.
@phdthesis{sogbohossou_ontologies_2002, type = {Thèse de maîtrise},
title = {Ontologies, vocabulaires {XML,} représentation et exploitation des connaissances dans le cadre d’une organisation},
abstract = {Les ontologies fournissent une compréhension commune d’un domaine qui peut être transmise entre des hommes et des systèmes d’application hétérogènes et distribués. Elles sont utilisées dans les échanges d’information pour répertorier et spécifier l'univers des informations qu'on peut échanger. Par ailleurs, la technologie Internet, en particulier le World Wide Web, est actuellement la principale infrastructure pour l’échange d’information en ligne. La technologie émergente dans ce domaine pour décrire la structure et la sémantique de l’information est {XML.} Les avantages de cette technologie sont exploités dans la gestion de la connaissance dans les organisations à travers des systèmes de gestion de connaissances comme {SHOE} et Ontobroker. Nous avons développé un système basé sur la même architecture et utilisant le langage {DAML+OIL.} Ce langage est en phase d’étude pour devenir le standard des ontologies du Web. L’architecture comprend une structure de base de données relationnelle pour stocker les ontologies, une application pour charger une ontologie dans la base et une application pour émettre des requêtes sur la base de données. Le modèle de développement par prototypage rapide est utilisé pour réaliser les applications concernées et la méthodologie {METHONTOLOGY} pour construire l'ontologie du département. Enfin, les limites de cette architecture, notamment du langage {DAML+OIL} face aux exigences du {W3C} concernant les ontologies corporatives ont été examinées. Notamment l’agrégation et les relations temporelles ont fait l’objet d’une étude approfondie.},
school = {Université du Québec à Montréal},
author = {Defodji Sogbohossou},
year = {2002},
keywords = {Ontologie},
pages = {152 p.},
annote = {{{\textless}p{\textgreater}sogbohossouDefodji2002.pdf{\textless}/p{\textgreater}}} },
-
OntoWeb, Deliverable 1.3 : a survey on ontology tools, 2002.
@misc{ontoweb_deliverable_2002, title = {Deliverable 1.3 : a survey on ontology tools},
url = {http://www.aifb.uni-karlsruhe.de/WBS/ysu/publications/OntoWeb_Del_1-3.pdf},
author = {{OntoWeb}},
month = may, year = {2002},
keywords = {Ontologie},
annote = {{\textless}p{\textgreater}ontoweb2002.pdf{\textless}/p{\textgreater}} },
-
Y. Ding and S. Foo, "Ontology research and development. Part 1 : a review of ontology generation," Journal of Information Science, vol. 28, iss. 2, pp. 123-136, 2002.
@article{ding_ontology_2002, title = {Ontology research and development. Part 1 : a review of ontology generation},
volume = {28},
abstract = {Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next generation of Web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This timely survey is presented in two parts. This first part reviews the state-of-the-art techniques and work done on semi- automatic and automatic ontology generation, as well as the problems facing these researches. The second complimentary survey is dedicated to ontology mapping and ontology evolving. Through this survey, we identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impedance to ontology learning and applicability. Further researches are encouraged to find appropriate and efficient ways to detect or identify relations through semi-automatic automatic means.},
number = {2},
journal = {Journal of Information Science},
author = {Ying Ding and Schubert Foo},
year = {2002},
keywords = {Ontologie, Web},
pages = {123--136},
annote = {{{\textless}p{\textgreater}dingYing2002\_partie1.pdf{\textless}/p{\textgreater}}} },
-
A. Maedche, G. Neumann, S. Staab, and J. Kacprzyk, "Bootstrapping an ontology based information extraction system." Springer, 2002.
@incollection{maedche_bootstrappingontology_2002, series = {Studies in fuzziness and soft computing; 111},
title = {Bootstrapping an ontology based information extraction system},
url = {http://citeseer.ist.psu.edu/maedche02bootstrapping.html},
abstract = {Automatic intelligent web exploration will benefit from shallow information extraction techniques if the latter can be brought to work within many different domains. The major bottleneck for this, however, lies in the so far difficult and expensive modeling of lexical knowledge, extraction rules, and an ontology that together define the information extraction system. In this paper we present a bootstrapping approach that allows for the fast creation of an ontology-based information extracting system relying on several basic components, viz. a core information extraction system, an ontology engineering environment and an inference engine. We make extensive use of machine learning techniques to support the semi-automatic, incremental bootstrapping of the domain-specific target information extraction system.},
booktitle = {Intelligent exploration of the Web},
publisher = {Springer},
author = {A. Maedche and G. Neumann and S. Staab and J. Kacprzyk},
year = {2002},
keywords = {Apprentissage machine, Extraction d'information, Ontologie} },
-
Y. Ding and S. Foo, "Ontology research and development. Part 2 : a review of ontology mapping and evolving," Journal of Information Science, vol. 28, iss. 5, pp. 375-388, 2002.
@article{ding_ontology_2002-1, title = {Ontology research and development. Part 2 : a review of ontology mapping and evolving},
volume = {28},
abstract = {This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far achieved limited success. Expert human input is essential in almost all cases. Achievements have been made largely in the form of tools and aids to assist the human expert. Many research challenges remain in this field and many of such challenges need to be overcome if the next generation of the Semantic Web is to be realized.},
number = {5},
journal = {Journal of Information Science},
author = {Ying Ding and Schubert Foo},
year = {2002},
keywords = {Ontologie},
pages = {375--388},
annote = {{{\textless}p{\textgreater}dingYing2002\_partie2.pdf{\textless}/p{\textgreater}}} },
-
I. Horrocks, "DAML+OIL : a reason-able Web ontology language," in Advances in database technology-EDBT 2002 : 8th international conference on extending database technology, Prague, Czech Republic, march 25-27, 2002 : proceedings, Berlin ; New York, NY, 2002, pp. 103-116.
@inproceedings{horrocks_daml+oil_2002, address = {Berlin ; New York, {NY}},
series = {Lecture notes in computer science; 2287},
title = {{DAML+OIL} : a reason-able Web ontology language},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14.2003&rep=rep1&type=pdf},
doi = {10.1.1.14.2003},
abstract = {Ontologies are set to play a key role in the {“Semantic} Web”, extending syntactic interoperability to semantic interoperability by providing a source of shared and precisely defined terms. {DAML+OIL} is an ontology language specifically designed for use on the Web; it exploits existing Web standards {(XML} and {RDF),} adding the familiar ontological primitives of object oriented and frame based systems, and the formal rigor of a very expressive description logic. The logical basis of the language means that reasoning services can be provided, both to support ontology design and to make {DAML+OIL} described Web resources more accessible to automated processes.},
booktitle = {Advances in database {technology-EDBT} 2002 : 8th international conference on extending database technology, Prague, Czech Republic, march 25-27, 2002 : proceedings},
publisher = {{Springer-Verlag}},
author = {Ian Horrocks},
year = {2002},
keywords = {Ontologie},
pages = {103--116},
annote = {{{\textless}p{\textgreater}horrocksIan2002.pdf{\textless}/p{\textgreater}}} },
-
N. Ogata, "A formal ontology discovery from Web documents," in Web intelligence : research and development : first Asia-Pacific conference, WI 2001 Maebashi City, Japan, october 23–26, 2001 : proceedings, Berlin; Heidelberg, 2001, pp. 514-519.
@inproceedings{ogata_formal_2001, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 2198},
title = {A formal ontology discovery from Web documents},
isbn = {978-3-540-42730-8},
url = {http://www.springerlink.com/content/7kma66jbjegcrvwu/},
doi = {10.1007/3-540-45490-X_66},
abstract = {This paper defines a framework of formal ontology that is compatible with domain-specificity that Web documents has, and natural language structures. Furthermore, this paper investigates how to extract information about the formal ontology of the domain written in Web documents based on logics, Web technology such as {XML} and natural language processing.},
booktitle = {Web intelligence : research and development : first {Asia-Pacific} conference, {WI} 2001 Maebashi City, Japan, october 23–26, 2001 : proceedings},
publisher = {Springer},
author = {Norihiro Ogata},
year = {2001},
keywords = {Ontologie},
pages = {514--519},
annote = {{{\textless}p{\textgreater}ogataNorihiro2001.pdf{\textless}/p{\textgreater}}} },
-
S. Tiun, R. Abdullah, and T. E. Kong, "Automatic topic identification using ontology hierarchy," in Computational linguistics and intelligent text processing : second international conference, CICLing 2001 Mexico City, Mexico, february 18–24, 2001 : proceedings, 2001, pp. 444-453.
@inproceedings{tiun_automatic_2001, series = {Lecture notes in computer science; 2004},
title = {Automatic topic identification using ontology hierarchy},
isbn = {3-540-41687-0},
url = {utmk.cs.usm.my/pdf/CICLing01.pdf},
booktitle = {Computational linguistics and intelligent text processing : second international conference, {CICLing} 2001 Mexico City, Mexico, february 18–24, 2001 : proceedings},
publisher = {{Springer-Verlag}},
author = {Sabrina Tiun and Rosni Abdullah and Tang Enya Kong},
year = {2001},
keywords = {Ontologie},
pages = {444--453},
annote = {{{\textless}p{\textgreater}tiunSabrina2001.pdf{\textless}/p{\textgreater}}} },
-
N. F. Noy and D. L. McGuiness, Ontology development 101 : a guide to creating your first ontology, 2001.
@misc{noy_ontology_2001, title = {Ontology development 101 : a guide to creating your first ontology},
url = {http://ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness.pdf},
abstract = {Ontologies have become core components of many large applications yet the training material has not kept pace with the growing interest. This paper addresses the issues of why one would build an ontology and presents a methodology for creating ontologies based on declarative knowledge representation systems. It leverages the two authors experiences building and maintaining ontologies in a number of ontology environments including Protege-2000, Ontolingua, and Chimaera. It presents the methodology by example utilizing a tutorial wines knowledge base example. While it is aimed at users of frame-based systems, it can be useful for building ontologies in any object-centered system.},
author = {Natalya F. Noy and Deborah L. {McGuiness}},
month = mar, year = {2001},
keywords = {Ontologie},
annote = {{{\textless}p{\textgreater}noyNatalya2001.pdf{\textless}/p{\textgreater}}} },
-
A. Maedche and S. Staab, "Ontology learning from text." Springer, 2001, pp. 364-364.
@incollection{maedche_ontology_2001, series = {Lecture notes in computer science; 1959},
title = {Ontology learning from text},
isbn = {0302-9743},
booktitle = {Natural language processing and information systems : 5th international conference on applications of natural language to information systems, {NLDB} 2000, Versailles, France, june 28-30, 2000 : revised papers},
publisher = {Springer},
author = {A. Maedche and S. Staab},
year = {2001},
keywords = {Apprentissage machine, Ontologie},
pages = {364--364} },
-
Y. Ding, "A review of ontologies with the semantic Web in view," Journal of Information Science, vol. 27, iss. 6, pp. 377-384, 2001.
@article{ding_review_2001, title = {A review of ontologies with the semantic Web in view},
volume = {27},
issn = {01655515},
abstract = {The World Wide Web is currently starting to move from the first generation to the second generation: the Semantic Web. Ontologies are the backbone for this Semantic Web. This paper aims to introduce and give the readers an overview of ontology in general. It discusses the definitions of ontology, kinds of ontology, ontology tools, ontology language and some important ontology projects, both current and completed.},
number = {6},
journal = {Journal of Information Science},
author = {Ying Ding},
year = {2001},
keywords = {Ontologie, Web sémantique},
pages = {377--384},
annote = {{{\textless}p{\textgreater}dingYing2001.pdf{\textless}/p{\textgreater}}} },
-
P. Clerkin, P. Cunningham, and C. Hayes, "Ontology discovery for the semantic web using hierarchical clustering." 2001.
@inproceedings{patrick_clerkin_ontology_2001, title = {Ontology discovery for the semantic web using hierarchical clustering},
url = {http://semwebmine2001.aifb.uni-karlsruhe.de/online/semwebmine03.pdf},
doi = {10.1.1.70.3731},
abstract = {According to a proposal by Tim {Berners-Lee,} the World Wide Web should be extended to make a Semantic Web where human understandable content is structured in such a way as to make it machine processable. Central to this conception is the establishment of shared ontologies, which specify the fundamental objects and relations important to particular online communities. Normally, such ontologies are hand crafted by domain experts. In this paper we propose that certain techniques employed in data mining tasks can be adopted to automatically discover and generate ontologies. In particular, we focus on the conceptual clustering algorithm, {COBWEB,} and show that it can be used to generate class hierarchies expressible in {RDF} Schema. We consider applications of this approach to online communities where recommendation of assets on the basis of user behaviour is the goal, illustrating our arguments with reference to the Smart Radio online song recommendation application.},
author = {Patrick Clerkin and Pádraig Cunningham and Conor Hayes},
year = {2001},
keywords = {Ontologie, Web sémantique},
annote = {{{\textless}p{\textgreater}clerkinPatrick2001.pdf{\textless}/p{\textgreater}}} },
-
M. Obitko, "Ontologies description and applications," Czech Technical University in Prague, Prague, Rapport de recherche GL 126/01, 2001.
@techreport{obitko_ontologies_2001, address = {Prague},
type = {Rapport de recherche},
title = {Ontologies description and applications},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.419&rep=rep1&type=pdf},
abstract = {The word “ontology” has gained a good popularity within the {AI} community. Ontology is usually viewed as a high-level description con- sisting of concepts that organize the upper parts of the knowledge base. However, meaning of the term “ontology” tends to be a bit vague, as the term is used in different ways. In this paper we will attempt to clarify the meaning of the ontology including the philosophical views and show why ontologies are useful and important. We will give an overview of ontology structures in several particular systems. A field proposed within ontological efforts, “ontological engi- neering”, will be also described. Usage of ontologies in several particular ways will be discussed. These include systems and ideas to support knowledge base sharing and reuse, both for computers and humans, ontology based communication in multi- agent systems, applications of ontologies for natural language processing, applications in documents search and enrichment of knowledge bases, both particularly for the World Wide Web environment and construction of educational systems, particularly intelligent tutoring systems.},
number = {{GL} 126/01},
institution = {Czech Technical University in Prague},
author = {Marek Obitko},
year = {2001},
keywords = {Ontologie},
pages = {35 p.},
annote = {{{\textless}p{\textgreater}obitkoMarek2001.pdf{\textless}/p{\textgreater}}} },
-
H. Stuckenschmidt and F. van Harmelen, "Ontology-based metadata generation from semistructured information." 2001, p. 440.
@inproceedings{stuckenschmidt_ontology-based_2001, title = {Ontology-based metadata generation from semistructured information},
url = {http://citeseer.ist.psu.edu/stuckenschmidt01ontologybased.html},
abstract = {Content-related metadata plays an important role in intelligent information systems. Especially on the world-wide web meaningful metadata describing the contents of a web-site is the key to intelligent retrieval and access of information. Metadata description standards like {RDF} and {RDF} schema have been developed and work in progress addresses the use of ontologies to provide a logical foundation for metadata. However, the acquisition of appropriate metadata is still a problem. The main part of the paper is concerned with the specification of ontologies and metadata models. We describe the Spectacle approach, a knowledge-based approach for metadata validation and generation as well as tools related to the ontology language {OIL.} We conclude that the specification of ontologies and the generation of metadata models are processes that supplement each other and propose a method for semi-automatic generation of metadata models on the basis of ontologies.},
publisher = {Sheridan Printing},
author = {H. Stuckenschmidt and F. van Harmelen},
year = {2001},
keywords = {Ontologie},
pages = {440---444} },
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P. Velardi, M. Missikoff, and R. Basili, "Identification of relevant terms to support the construction of domain ontologies," , Toulouse, France, 2001, pp. 1-8.
@inproceedings{velardi_identification_2001, address = {Toulouse, France},
title = {Identification of relevant terms to support the construction of domain ontologies},
volume = {Volume 2001},
url = {http://portal.acm.org/citation.cfm?doid=1118220.1118225},
abstract = {Though the utility of domain Ontologies is now widely acknowledged in the {IT} {(Information} Technology) community, several barriers must be overcome before Ontologies become practical and useful tools. One important achievement would be to reduce the cost of identifying and manually entering several thousand-concept descriptions. This paper describes a text mining technique to aid an Ontology Engineer to identify the important concepts in a Domain Ontology.},
author = {Paola Velardi and Michele Missikoff and Roberto Basili},
year = {2001},
keywords = {Ontologie},
pages = {1 -- 8} },
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A. Maedche and S. Staab, "Semi-automatic engineering of ontologies from text," , Chicago, USA, 2000.
@inproceedings{maedche_semi-automatic_2000, address = {Chicago, {USA}},
title = {Semi-automatic engineering of ontologies from text},
url = {http://citeseer.ist.psu.edu/maedche00semiautomatic.html},
abstract = {Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, efforts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We present a general architecture for discovering conceptual structures and engineering ontologies. Based on the architecture we propose a new approach to extend current approaches, who mostly focus on the semi-automatic acquisition of taxonomies, by the discovery of non-taxonomic conceptual relations. We use a generalized association rule algorithm that does not only detect relations between concepts, but also determines the appropriate level of abstraction at which to dene relations.},
author = {A. Maedche and S. Staab},
year = {2000},
keywords = {Fouille de texte, Ontologie} },
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N. Aussenac-Gilles, B. Biebow, and S. Szulman, "Revisiting ontology design : a method based on corpus analysis," in Knowledge engineering and knowledge management methods, models, and tools : 12th international conference, EKAW 2000 Juan-les-Pins, France, october 2–6, 2000 : proceedings, Berlin; Heidelberg, 2000, pp. 27-66.
@inproceedings{aussenac-gilles_revisiting_2000, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 1937. Lecture notes in artificial intelligence},
title = {Revisiting ontology design : a method based on corpus analysis},
isbn = {0302-9743},
abstract = {We promote a new approach for knowledge modelling based on knowledge elicitation from technical documents. It benefits of the increasing amount of available electronic texts and of the maturity of natural language processing tools. The approach defines a framework where the knowledge engineer selects the appropriate tools, combines their use and interprets their results to build up a domain model. The paper presents the method and reports an on-going application to design an ontology of knowledge engineering tools in French.},
booktitle = {Knowledge engineering and knowledge management methods, models, and tools : 12th international conference, {EKAW} 2000 {Juan-les-Pins,} France, october 2–6, 2000 : proceedings},
publisher = {Springer},
author = {N. {Aussenac-Gilles} and B. Biebow and S. Szulman},
year = {2000},
keywords = {Analyse de corpus, Design, Ontologie},
pages = {27--66} },
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A. Maedche and S. Staab, "Mining ontologies from text." 1937: , 2000, pp. 189-202.
@incollection{maedche_mining_2000, address = {1937},
series = {Lecture Notes in Artificial Intelligence},
title = {Mining ontologies from text},
isbn = {0302-9743},
abstract = {Ontologies have become an important means for structuring knowledge and building knowledge-intensive systems. For this purpose, efforts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We present a general architecture for discovering conceptual structures and engineering ontologies. Based on our generic architecture we describe a case study for mining ontologies from text using methods based on dictionaries and natural language text. The case study has been carried out in the telecommunications domain. Supporting the overall text ontology engineering process, our comprehensive approach combines dictionary parsing mechanisms for acquiring a domain-specific concept taxonomy with a discovery mechanism for the acquisition of non-taxonomic conceptual relations.},
booktitle = {Knowledge Engineering and Knowledge Management, Proceedings},
author = {A. Maedche and S. Staab},
year = {2000},
keywords = {Fouille de texte, Ontologie},
pages = {189--202} },
-
D. Forest and Jean-Guy, "La classification mathématique des textes : un outil d’assistance à la lecture et à l’analyse des textes philosophiques," , 2000.
@article{forest_la_2000, series = {5es journées internationales d'analyse statistique des données textuelles},
title = {La classification mathématique des textes : un outil d'assistance à la lecture et à l'analyse des textes philosophiques},
author = {Dominic Forest and {Jean-Guy} Meunier},
month = mar, year = {2000},
keywords = {Analyse de texte, Classification, Fouille de texte, Philosophie} },
-
M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, and S. Slattery, "Learning to construct knowledge bases from the World Wide Web," Artificial Intelligence, vol. 118, iss. 1-2, pp. 69-113, 2000.
@article{craven_learning_2000, title = {Learning to construct knowledge bases from the World Wide Web},
volume = {118},
url = {http://www.sciencedirect.com/science/article/B6TYF-43FX0XK-3/2/8610b9d209e3e80a5ea6dfb53abdd711},
abstract = {The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer understandable knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more effective retrieval of Web information, and promote new uses of the Web to support knowledge-based inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs. The first is an ontology that defines the classes (e.g., , , , ) and relations (e.g., , ) of interest when creating the knowledge base. The second is a set of training data consisting of labeled regions of hypertext that represent instances of these classes and relations. Given these inputs, the system learns to extract information from other pages and hyperlinks on the Web. This article describes our general approach, several machine learning algorithms for this task, and promising initial results with a prototype system that has created a knowledge base describing university people, courses, and research projects.},
number = {1-2},
journal = {Artificial Intelligence},
author = {Mark Craven and Dan {DiPasquo} and Dayne Freitag and Andrew {McCallum} and Tom Mitchell and Kamal Nigam and Sean Slattery},
year = {2000},
keywords = {Apprentissage machine, Classification, Extraction d'information, Ontologie, Web},
pages = {69--113} },
-
A. Maedche and S. Staab, "The TEXT-TO-ONTO ontology learning environment," PhD Thesis , 2000.
@phdthesis{maedche_text-to-onto_2000, title = {The {TEXT-TO-ONTO} ontology learning environment},
url = {http://citeseer.ist.psu.edu/275146.html},
abstract = {Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, we present the {TEXT-TO-ONTO} Ontology Learning Environment, which is based on a general architecture for discovering conceptual structures and engineering ontologies from text. Our Ontology Learning Environment supports as well the acquisition of conceptual structures as mapping linguistic resources to the acquired structures. 1 Introduction Ontologies 1 have shown their usefulness in application areas such as intelligent information integration, information brokering and natural-language processing, to name but a few. However, their wide-spread usage is still hindered by ontology engineering being rather time-consuming and, hence, expensive. Our system {TEXT-TO-ONT...}},
author = {Alexander Maedche and Steffen Staab},
year = {2000},
keywords = {Ontologie} },
-
A. Maedche and S. Staab, "Discovering conceptual relations from text," Institute AIFB, Karlsruhe University2000.
@techreport{maedche_discovering_2000, title = {Discovering conceptual relations from text},
url = {http://citeseer.ist.psu.edu/maedche00discovering.html},
abstract = {Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, efforts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We broaden these investigations with regard to two dimensions. First, we present a general architecture for discovering ontological concepts and relations. This architecture is general enough to subsume current approaches in this direction. Second, we propose a new approach to extend current approaches, who mostly focus on the semi-automatic acquisition of taxonomies, by the discovery of non-taxonomic conceptual relations. We use a generalized association rule algorithm that does not only detect relations between concepts, but also de...},
institution = {Institute {AIFB,} Karlsruhe University},
author = {Alexander Maedche and Steffen Staab},
year = {2000},
keywords = {Ontologie},
pages = {321---325} },
-
J. F. Sowa, "Ontology, metadata, and semiotics," in Conceptual Structures: Logical, Linguistic, and Computational issues : 8th international conference on conceptual structures, ICCS 2000, Darmstadt, Germany, august 14-18, 2000 : proceedings, Berlin; Heidelberg, 2000, pp. 55-81.
@inproceedings{sowa_ontology_2000, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 1867},
title = {Ontology, metadata, and semiotics},
isbn = {{3-540-67859-X}},
url = {http://portal.acm.org/citation.cfm?id=645493.657527},
booktitle = {Conceptual Structures: Logical, Linguistic, and Computational issues : 8th international conference on conceptual structures, {ICCS} 2000, Darmstadt, Germany, august 14-18, 2000 : proceedings},
publisher = {{Springer-Verlag}},
author = {John F. Sowa},
year = {2000},
keywords = {Ontologie},
pages = {55--81},
annote = {{{\textless}p{\textgreater}sowaJohn2000.pdf{\textless}/p{\textgreater}}} },
-
C. Heng-Hsou, K. Yau-Hwang, and H. Jang-Pong, "An event-driven and ontology-based approach for the delivery and information extraction of e-mails," , Taipei, Taiwan, 2000, pp. 103-9.
@inproceedings{heng-hsou_event-driven_2000, address = {Taipei, Taiwan},
series = {Proceedings International Symposium on Multimedia Software Engineering},
title = {An event-driven and ontology-based approach for the delivery and information extraction of e-mails},
url = {http://dx.doi.org/10.1109/MMSE.2000.897199},
abstract = {In the field of information extraction {(IE),} the extraction of information from documents is usually event-oriented. Therefore, many information extraction machines have built their domain knowledge based on events. However, information extraction is often limited in its application in specific domains, because the events are simply detected by predefined keywords. We propose event detection driven intelligent information extraction by using the neural network paradigm. In this paper, the backpropagation {(BP)} learning algorithm is adopted to train the event detector. In order to detect the potential events in documents effectively, we apply natural language processing technology to aid the selection of nouns as feature words. Unrelated nouns are filtered by the analysis based on document frequency distribution. Finally, selected nouns are conceptualized into concepts. These concepts are supposed to characterize documents appropriately and they are stored in ontology as a knowledge base. In the experimental results, we achieved high accuracy both in the inside testing and outside testing of Internet documents. By means of the well-trained event detector, the information extraction task can be certainly applied in wider domains. Eventually, this event detection technology is introduced for the delivery and information extraction of e-mail},
publisher = {{IEEE} Comput. Soc},
author = {Chang {Heng-Hsou} and Ko {Yau-Hwang} and Hsu {Jang-Pong}},
year = {2000},
note = {Copyright 2001, {IEE}},
keywords = {Extraction d'information, Ontologie},
pages = {103--9},
annote = {{\textless}p{\textgreater}6806550 event-driven approach ontology-based approach intelligent information extraction e-mail neural network backpropagation learning natural language processing document frequency distribution experiment Internet{\textless}/p{\textgreater}} },
-
J. B. Copeland, "The turing test*," Minds and machines, vol. 10, iss. 4, pp. 519-539, 2000.
@article{copeland_turing_2000, title = {The turing test*},
volume = {10},
issn = {0924-6495 {(Print)} 1572-8641 {(Online)}},
url = {http://www.springerlink.com/content/w103433h4g273841/},
doi = {10.1023/A:1011285919106},
abstract = {Turing's test has been much misunderstood. Recently unpublished material by Turing casts fresh light on his thinking and dispels a number of philosophical myths concerning the Turing test. Properly understood, the Turing test withstands objections that are popularly believed to be fatal.},
number = {4},
journal = {Minds and machines},
author = {B. Jack Copeland},
month = nov, year = {2000},
keywords = {Philosophie},
pages = {519--539},
annote = {{{\textless}p{\textgreater}copelandB2000.pdf{\textless}/p{\textgreater}}} },
-
O. Corcho and A. Gómez-Pérez, "A roadmap to ontology specification languages," in Knowledge engineering and knowledge management methods, models, and tools : 12th international conference, EKAW 2000 Juan-les-Pins, France, october 2–6, 2000 : proceedings, Berlin; Heidelberg, 2000, pp. 80-96.
@inproceedings{corcho_roadmap_2000, address = {Berlin; Heidelberg},
series = {Lecture notes in computer science; 1937},
title = {A roadmap to ontology specification languages},
isbn = {3540411194},
booktitle = {Knowledge engineering and knowledge management methods, models, and tools : 12th international conference, {EKAW} 2000 {Juan-les-Pins,} France, october 2–6, 2000 : proceedings},
publisher = {Springer},
author = {Oscar Corcho and Asunción {Gómez-Pérez}},
year = {2000},
keywords = {Ontologie},
pages = {80--96},
annote = {{{\textless}p{\textgreater}corchoOscar2000.pdf{\textless}/p{\textgreater}}} },
-
R. E. Kent, "Conceptual knowledge markup language : an introduction," Netnomics, vol. 2, pp. 139-169, 2000.
@article{kent_conceptual_2000, title = {Conceptual knowledge markup language : an introduction},
volume = {2},
url = {http://www.ingentaconnect.com/content/klu/netn/2000/00000002/00000002/00329147},
abstract = {Conceptual Knowledge Markup Language {(CKML)} is an application of {XML.} Earlier versions of {CKML} followed rather exclusively the philosophy of Conceptual Knowledge Processing {(CKP),} a principled approach to knowledge representation and data analysis that “advocates methods and instruments of conceptual knowledge processing which support people in their rational thinking, judgment and acting and promote critical discussion”. The new version of {CKML} continues to follow this approach, but also incorporates various principles, insights and techniques from Information Flow {(IF),} the logical design of distributed systems. Among other things, this allows diverse communities of discourse to compare their own information structures, as coded in logical theories, with that of other communities that share a common generic ontology. {CKML} incorporates the {CKP} ideas of concept lattice and formal context, along with the {IF} ideas of classification (= formal context), infomorphism, theory, interpretation and local logic. Ontology Markup Language {(OML),} a subset of {CKML} that is a self-sufficient markup language in its own right, follows the principles and ideas of Conceptual Graphs {(CG).} {OML} is used for structuring the specifications and axiomatics of metadata into ontologies. {OML} incorporates the {CG} ideas of concept, conceptual relation, conceptual graph, conceptual context, participants and ontology. The link from {OML} to {CKML} is the process of conceptual scaling, which is the interpretive transformation of ontologically structured knowledge to conceptual structured knowledge.},
journal = {Netnomics},
author = {R. E. Kent},
year = {2000},
keywords = {Classification, Ontologie},
pages = {139--169} },
-
M. Liao, A. Abecker, A. Bernardi, K. Hinkelmann, and M. Sintek, "Ontologies for knowledge retrieval in organizational memories," Proceeding workshop on learning software organizations, Fraunhofer Institute for experimental software engineering, pp. 11-25, 1999.
@article{liao_ontologies_1999, title = {Ontologies for knowledge retrieval in organizational memories},
url = {http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=72960792ED0D4CD22FF35E93ADD56B21?doi=10.1.1.30.5198&rep=rep1&type=pdf},
doi = {10.1.1.30.5198},
abstract = {An Organizational Memory {(OM)} captures, stores and disseminates valuable corporate knowledge and is thus a central prerequisite for enterprise knowledge management. For structuring, accessing, and maintaining large amounts of heterogeneous information, appropriate meta-level descriptions are needed which specify the structure, content, and potential usage of the object-level knowledge. Such meta-level descriptions are provided for data in the form of data models, for formal knowledge as ontologies, and for informal documents as document descriptors. In this paper, we sketch an ontology-based approach for comprehensive meta-modeling and retrieval of heterogeneous data, formal knowledge, and documents. We identify information ontology, domain ontology, and enterprise ontology as main contributors to a vocabulary for comprehensive information meta modeling. We elaborate a bit on the underlying representation formalism, sketch a sample scenario, and present ontology-based heuristic retrieval in the organizational memory.},
journal = {Proceeding workshop on learning software organizations, Fraunhofer Institute for experimental software engineering},
author = {Minghong Liao and Andreas Abecker and Ansgar Bernardi and Knut Hinkelmann and Michael Sintek},
year = {1999},
keywords = {Gestion des connaissances, Ontologie, Recherche d'information},
pages = {11--25},
annote = {{{\textless}p{\textgreater}liaoMinghong1999.pdf{\textless}/p{\textgreater}}} },
-
D. Jones, T. Bench-capon, and P. Visser, "Methodologies for ontology development," in IT \& knows information technologies and knowledge systems : proceedings of the XV. IFIP World Computer Congress, 31 Aug.-4 Sept. 1998, Vienna, Austria and Budapest, Hungary, Vienne, Autriche, 1998, pp. 62-75.
@inproceedings{jones_methodologies_1998, address = {Vienne, Autriche},
title = {Methodologies for ontology development},
isbn = {{385403122X}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.2437&rep=rep1&type=pdf},
doi = {10.1.1.52.2437},
abstract = {It is now widely recognised that constructing a domain model, or ontology, is an important step in the development of knowledge based systems. What is lacking, however, is a clear understanding of how to build ontologies. In this paper we survey the work which has been done so far in beginning to provide a methodology for building ontologies. This work is still formative, and relies heavily on particular experiences. We also provide some discussion of this work, and identify the key issues that must be addressed if we are to move on from ontology construction being an art and to make it an understood engineering process.},
booktitle = {{IT} \& knows information technologies and knowledge systems : proceedings of the {XV.} {IFIP} World Computer Congress, 31 Aug.-4 Sept. 1998, Vienna, Austria and Budapest, Hungary},
publisher = {Austrian Computer Society {(OCG)} on behalf of the International Federation for Information Processing {(IFIP)}},
author = {Dean Jones and Trevor Bench-capon and Pepijn Visser},
year = {1998},
keywords = {Méthodologie, Ontologie},
pages = {62--75},
annote = {{{\textless}p{\textgreater}jonesDean1998.pdf{\textless}/p{\textgreater}}} },
-
P.E. and N.J.I., "Bottom-up construction of ontologies," Knowledge and Data Engineering, IEEE Transactions on, vol. 10, iss. 4, pp. 513-526, 1998.
@article{van_der_vet_bottom-up_1998, title = {Bottom-up construction of ontologies},
volume = {10},
issn = {1041-4347},
url = {http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel4/69/15318/00706054.pdf?tp=&isnumber=15318&arnumber=706054&punumber=%3Cb%3E%3Cfont%20color=990000%3E69%3C/font%3E%3C/b%3E},
doi = {10.1109/69.706054},
abstract = {Presents a particular way of building ontologies that proceeds in a bottom-up fashion. Concepts are defined in a way that mirrors the way their instances are composed out of smaller objects. The smaller objects themselves may also be modeled as being composed. Bottom-up ontologies are flexible through the use of implicit and, hence, parsimonious part-whole and subconcept-superconcept relations. The bottom-up method complements current practice, where, as a rule, ontologies are built top-down. The design method is illustrated by an example involving ontologies of pure substances at several levels of detail. It is not claimed that bottom-up construction is a generally valid recipe; indeed, such recipes are deemed uninformative or impossible. Rather, the approach is intended to enrich the ontology developer's toolkit},
number = {4},
journal = {Knowledge and Data Engineering, {IEEE} Transactions on},
author = {{P.E.} van der Vet and {N.J.I.} Mars},
year = {1998},
keywords = {Bottom-up, Ontologie},
pages = {513--526},
annote = {{{\textless}p{\textgreater}van\_der\_vetPaul1998.pdf{\textless}/p{\textgreater}}} },
-
N. Lacharité, Lisons KantUniversité de Montréal, 1997.
@misc{lacharit_lisons_1997, title = {Lisons Kant},
url = {http://classiques.uqac.ca/classiques/kant_emmanuel/documents_connexes/Lacharite_Lisons_Kant.pdf},
publisher = {Université de Montréal},
author = {Normand Lacharité},
month = may, year = {1997},
keywords = {Philosophie},
annote = {{{\textless}p{\textgreater}lachariteNormand1997.pdf{\textless}/p{\textgreater}}} },
-
M. Uschold and M. Gruninger, "Ontologies : principles, methods and applications," Knowledge engineering review, vol. 11, p. 93, 1996.
@article{uschold_ontologies_1996, title = {Ontologies : principles, methods and applications},
volume = {11},
shorttitle = {Ontologies},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.48.5917&rep=rep1&type=pdf},
doi = {10.1.1.48.5917},
abstract = {This paper is intended to serve as a comprehensive introduction to the emerging field concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to effective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an 'ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, interoperability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing definitions. We then consider the benefits of and describe, a more formal approach. We revisit the scoping phase, and discuss the role of formal languages and techniques in the specification, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging field, considering various case studies, software tools for ontology development, key research issues and future prospects.},
journal = {Knowledge engineering review},
author = {Mike Uschold and Michael Gruninger},
year = {1996},
keywords = {Méthodologie, Ontologie},
pages = {93---136},
annote = {{{\textless}p{\textgreater}uscholdMike1996.pdf{\textless}/p{\textgreater}}} },
-
Hans-Georg, Vérité et méthode : les grandes lignes d’une herméneutique philosophique, Paris: Seuil, 1996.
@book{gadamer_vrit_1996, address = {Paris},
title = {Vérité et méthode : les grandes lignes d'une herméneutique philosophique},
publisher = {Seuil},
author = {{Hans-Georg} Gadamer},
year = {1996},
keywords = {Méthodologie, Philosophie} },
-
N. Lacharité, Conflits de modèles en théories de la représentation, 1994.
@misc{lacharit_conflits_1994, title = {Conflits de modèles en théories de la représentation},
author = {Normand Lacharité},
month = oct, year = {1994},
keywords = {Philosophie},
annote = {{{\textless}p{\textgreater}lachariteNormand1994.pdf{\textless}/p{\textgreater}}} },
-
T. R. Gruber, "A translation approach to portable ontology specifications," Knowledge Acquisition, vol. 5, iss. 2, pp. 199-220, 1993.
@article{gruber_translation_1993, title = {A translation approach to portable ontology specifications},
volume = {5},
abstract = {To support the sharing and reuse of formally represented knowledge among {AI} systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.},
number = {2},
journal = {Knowledge Acquisition},
author = {Thomas R. Gruber},
year = {1993},
keywords = {Ontologie},
pages = {199--220},
annote = {{{\textless}p{\textgreater}gruberThomas1993.pdf{\textless}/p{\textgreater}}} },
-
J. Boulad-Ayoub, Fiches pour l’étude de Kant, Presses de l’université du Québec, 1990.
@book{boulad-ayoub_fiches_1990, series = {Cahiers recherches et théories},
title = {Fiches pour l'étude de Kant},
isbn = {2920884182},
url = {http://classiques.uqac.ca/classiques/kant_emmanuel/documents_connexes/Ajoub_fiches_pour_Kant.pdf},
abstract = {Les fiches présentées ici devraient servir d’adjuvant au travail personnel de l’étudiant désireux d’aborder la philosophie de Kant. Elles offrent un balisage de la démarche kantienne plutôt qu’une analyse serrée de ses ouvrages. Nous nous sommes surtout attaché à éclairer les points névralgiques de sa réflexion, à montrer l’origine des problèmes qui se sont posés à Kant, comment ils se présentaient, à indiquer les solutions que {l’Aufklärer} leur a apportées et les positions qu’il a développées.},
number = {S 18},
publisher = {Presses de l'université du Québec},
author = {Josiane {Boulad-Ayoub}},
year = {1990},
note = {Une bibliothèque numérique unique et originale dans le monde francophone en sciences sociales et humaines, développée en collaboration avec {l'Université} du Québec à Chicoutimi, fondée et dirigée par {Jean-Marie} Tremblay, bénévole, professeur de sociologie au département des sciences humaines du Cégep de Chicoutimi.},
keywords = {Philosophie},
annote = {{{\textless}p{\textgreater}boulad-ayoubJosiane1990.pdf{\textless}/p{\textgreater}}} },
-
W. Iser, L’acte de lecture : théorie de l’effet esthétique, Brusselle: Mardaga, 1985.
@book{iser_lacte_1985, address = {Brusselle},
title = {L'acte de lecture : théorie de l'effet esthétique},
publisher = {Mardaga},
author = {Wolfgang Iser},
year = {1985},
keywords = {Philosophie} },
-
G. Rodis-Lewis, Descartes, Paris: Librarie générale française, 1984.
@book{rodis-lewis_descartes_1984, address = {Paris},
title = {Descartes},
publisher = {Librarie générale française},
author = {Geneviève {Rodis-Lewis}},
year = {1984},
keywords = {Philosophie} },
-
A. McKinnon, "Some conceptual ties in Descartes’ meditations," Dialogue, vol. 18, pp. 166-174, 1979.
@article{mckinnon_conceptual_1979, title = {Some conceptual ties in Descartes' meditations},
volume = {18},
journal = {Dialogue},
author = {Alastair {McKinnon}},
year = {1979},
keywords = {Philosophie},
pages = {166--174} },
-
N. Chomsky and J. J. Katz, "On innateness : a reply to Cooper," The Philosophical Review, vol. 84, iss. 1, pp. 70-87, 1975.
@article{chomsky_innateness_1975, title = {On innateness : a reply to Cooper},
volume = {84},
issn = {00318108},
url = {http://www.jstor.org/stable/2184081},
number = {1},
journal = {The Philosophical Review},
author = {Noam Chomsky and Jerrold J. Katz},
year = {1975},
note = {{ArticleType:} primary\_article / Full publication date: Jan., 1975 / Copyright © 1975 Cornell University},
keywords = {Philosophie},
pages = {70--87},
annote = {{{\textless}p{\textgreater}chomskyNoam1975.pdf{\textless}/p{\textgreater}}} },
-
A. McKinnon, "The conquest of fate in Kierkegaard," Dialogue, vol. 7, pp. 219-237, 1973.
@article{mckinnon_conquest_1973, title = {The conquest of fate in Kierkegaard},
volume = {7},
journal = {Dialogue},
author = {Alastair {McKinnon}},
year = {1973},
keywords = {Philosophie},
pages = {219--237} },
-
A. Robinet, "Courte introduction aux relations de la philosophie avec l’informatique," Dialectica, vol. 25, iss. 3-4, pp. 239-249, 1971.
@article{robinet_courte_1971, title = {Courte introduction aux relations de la philosophie avec l'informatique},
volume = {25},
url = {http://www3.interscience.wiley.com/cgi-bin/fulltext/120077143/PDFSTART},
doi = {10.1111/j.1746-8361.1971.tb00602.x},
number = {3-4},
journal = {Dialectica},
author = {André Robinet},
year = {1971},
keywords = {Informatique, Philosophie},
pages = {239--249},
annote = {{{\textless}p{\textgreater}robinetAndre1971.pdf{\textless}/p{\textgreater}}} },
-
P. Tombeur, "Le traitement automatique de textes philosophiques," Dialectica, vol. 25, iss. 3-4, pp. 261-273, 1971.
@article{tombeur_le_1971, title = {Le traitement automatique de textes philosophiques},
volume = {25},
url = {http://www3.interscience.wiley.com/cgi-bin/fulltext/120077145/PDFSTART},
doi = {10.1111/j.1746-8361.1971.tb00604.x},
abstract = {Ľarticle décrit brièvement les méthodes du Centre de Traitement Electronique des Documents de {ľUniversité} Catholique de Louvain. La pratique courante de ce Centre est de procéder par enregistrements intégraux; ceux-ci peuvent être soumis à des analyses diverses (essentiellement lexicographiques, morphologiques, syntaxiques et stylistiques) présentant des niveaux ďapprofondissement croissants. Ľauteur évoque rapidement les principaux résultats réalisables grâce aux programmes ďordinateur dès à présent opérationnels. Il met en relief ľintérêt que présente ľapplication de ces méthodes aux textes philosophiques et note les travaux réalisés au Centre en ce domaine. {ABSTRACT} Abstract},
number = {3-4},
journal = {Dialectica},
author = {Paul Tombeur},
year = {1971},
keywords = {Fouille de texte, Philosophie},
pages = {261--273},
annote = {{{\textless}p{\textgreater}tombeurPaul1971.pdf{\textless}/p{\textgreater}}} },
-
A. Robinet, "Descartes à l’ordinateur," Les études philosophiques, vol. 2, pp. 219-223, 1970.
@article{robinet_descartes_1970, title = {Descartes à l'ordinateur},
volume = {2},
journal = {Les études philosophiques},
author = {André Robinet},
year = {1970},
keywords = {Informatique, Philosophie},
pages = {219--223} },
-
N. Chomsky, "Philosophers and public philosophy," Ethics, vol. 79, iss. 1, pp. 1-9, 1968.
@article{chomsky_philosophers_1968, title = {Philosophers and public philosophy},
volume = {79},
issn = {00141704},
url = {http://www.jstor.org/page/termsConfirm.jsp?redirectUri=/stable/pdfplus/2379186.pdf},
number = {1},
journal = {Ethics},
author = {Noam Chomsky},
year = {1968},
keywords = {Philosophie},
pages = {1--9},
annote = {{{\textless}p{\textgreater}chomskyNoam1968.pdf{\textless}/p{\textgreater}}} },
-
A. McKinnon, "La philosophie et les ordinateurs," Dialogue, vol. 7, iss. 1, pp. 219-237, 1968.
@article{mckinnon_la_1968, title = {La philosophie et les ordinateurs},
volume = {7},
number = {1},
journal = {Dialogue},
author = {Alastair {McKinnon}},
year = {1968},
keywords = {Informatique, Philosophie},
pages = {219--237} },
-
G. Rodis-Lewis, L’oeuvre de Descartes, Paris: Presses universitaires de France, 1966.
@book{rodis-lewis_loeuvre_1966, address = {Paris},
title = {L'oeuvre de Descartes},
publisher = {Presses universitaires de France},
author = {Geneviève {Rodis-Lewis}},
year = {1966},
keywords = {Philosophie} },
-
U. Eco, L’oeuvre ouverte, Paris: Seuil, 1965.
@book{eco_loeuvre_1965, address = {Paris},
title = {L'oeuvre ouverte},
publisher = {Seuil},
author = {Umberto Eco},
year = {1965},
keywords = {Philosophie} },
-
M. Heidegger, Being and time, San Francisco: Harper and Row, 1962.
@book{heidegger_being_1962, address = {San Francisco},
title = {Being and time},
publisher = {Harper and Row},
author = {Martin Heidegger},
year = {1962},
keywords = {Philosophie} },
-
M. C. Beardsley, Aesthetics : problems in the philosophy of criticism, New York: Harcourt, Brace \& World, 1958.
@book{beardsley_aesthetics_1958, address = {New York},
title = {Aesthetics : problems in the philosophy of criticism},
publisher = {Harcourt, Brace \& World},
author = {Monroe C. Beardsley},
year = {1958},
keywords = {Philosophie} },
-
B. de Spinoza, "Traité des autorités théologique et politique." Paris: Gallimard, 1954.
@incollection{spinoza_trait_1954, address = {Paris},
series = {Bibliothèque de la Pléiade},
title = {Traité des autorités théologique et politique},
booktitle = {Oeuvres complètes},
publisher = {Gallimard},
author = {Baruch de Spinoza},
year = {1954},
keywords = {Philosophie} },
-
S. Thompson, The folktale, Berkeley: University of California Press, 1946.
@book{thompson_folktale_1946, address = {Berkeley},
title = {The folktale},
publisher = {University of California Press},
author = {Stith Thompson},
year = {1946},
keywords = {Philosophie} },
-
V. Propp, Morphology of the folktale, Austin: University of Texas Press, 1928.
@book{propp_morphology_1928, address = {Austin},
title = {Morphology of the folktale},
publisher = {University of Texas Press},
author = {Vladimir Propp},
year = {1928},
keywords = {Analyse de texte, Philosophie} },
-
B. Tomashevsky, L. T. Lemon, and M. J. Reis, "Thematics." Lincoln: University of Nebraska Press, 1925, pp. 61-98.
@incollection{tomashevsky_thematics_1925, address = {Lincoln},
title = {Thematics},
booktitle = {Russian formalist criticism},
publisher = {University of Nebraska Press},
author = {Boris Tomashevsky and L. T. Lemon and M. J. Reis},
year = {1925},
keywords = {Philosophie},
pages = {61--98} },
-
N. Macchiavel, Le prince, Paris: T. Quinet, 1640.
@book{macchiavel_le_1640, address = {Paris},
title = {Le prince},
copyright = {domaine public},
url = {ftp://ftp.bnf.fr/008/N0082928_PDF_1_-1.pdf},
publisher = {T. Quinet},
author = {Nicolas Macchiavel},
year = {1640},
keywords = {Philosophie},
annote = {{{\textless}p{\textgreater}macchiavelNicolas1640.pdf{\textless}/p{\textgreater}}} },
-
B. Nguyen, M. Vazirgianis, I. Varlamis, and M. Halkidi, "Organising Web documents into thematic subsets using an ontology (THESUS)."
@article{nguyen_organising_????, title = {Organising Web documents into thematic subsets using an ontology {(THESUS)}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14.9749&rep=rep1&type=pdf},
doi = {10.1.1.14.9749},
abstract = {We describe in this article the architecture and of a system whose goal is to organise Web documents into clusters. We use incoming links to find accurate keywords describing each document. We assume an ontology of the domain we are interested in, and we use a thesaurus {(WordNet)} in order to map the keywords that describe a document to terms of the ontology. We then cluster the documents using a novel similarity measure, and a modified version of the incremental {DB-Scan} algorithm.},
author = {B. Nguyen and M. Vazirgianis and I. Varlamis and M. Halkidi},
keywords = {Document numérique, Ontologie},
annote = {{{\textless}p{\textgreater}nguyenB.pdf{\textless}/p{\textgreater}}} },
-
N. F. Noy, Ontology engineering for the semantic Web and beyond.
@misc{noy_ontology_????, type = {{PowerPoint}},
title = {Ontology engineering for the semantic Web and beyond},
url = {http://www.ip-super.org/res/related/OntologyEngineering.ppt},
author = {Natalya F. Noy},
keywords = {Ontologie, Web sémantique},
annote = {{{\textless}p{\textgreater}noyNatalya.ppt{\textless}/p{\textgreater}}} },
-
B. Hufschmitt and A. Lelu, Organisation d’un corpus philosophique, les oeuvres (françaises) de Descartes.
@misc{hufschmitt_organisation_????, title = {Organisation d'un corpus philosophique, les oeuvres (françaises) de Descartes},
url = {www.sdc2006.org/cdrom/contributions/Hufschmitt-isko-sdc.pdf},
author = {Benoit Hufschmitt and Alain Lelu},
keywords = {Analyse de corpus, Philosophie},
annote = {{{\textless}p{\textgreater}hufschmittBenoit.pdf{\textless}/p{\textgreater}}} },
-
C. Shirky, Ontology is overrated : categories, links, and tags.
@misc{shirky_ontology_????, title = {Ontology is overrated : categories, links, and tags},
url = {http://shirky.com/writings/ontology_overrated.html},
abstract = {This piece is based on two talks I gave in the spring of 2005 -- one at the {O'Reilly} {ETech} conference in March, entitled {"Ontology} Is Overrated", and one at the {IMCExpo} in April entitled {"Folksonomies} \& Tags: The rise of user-developed classification." The written version is a heavily edited concatenation of those two talks.},
author = {Clay Shirky},
keywords = {Ontologie},
howpublished = {http://shirky.com/writings/ontology\_overrated.html} },
-
A. Gómez-Pérez, M. Fernández-López, and O. Corcho, Ontological engineering : the role of ontologies in the semantic web.
@misc{gmez-prez_ontological_????, type = {{PDF} d'une présentation {PowerPoint}},
title = {Ontological engineering : the role of ontologies in the semantic web},
author = {Asunción {Gómez-Pérez} and Mariano {Fernández-López} and Oscar Corcho},
keywords = {Ontologie, Web sémantique},
annote = {{{\textless}p{\textgreater}gomez-perezAsuncion.pdf{\textless}/p{\textgreater}}} },
L. Wanner, R. Baeza-Yates, S. Brugmann, J. Codina, B. Diallo, E. Escorsa, M. Giereth, Y. Kompatsiaris, S. Papadopoulos, E. Pianta, G. Piella, I. Puhlmann, G. Rao, M. Rotard, P. Schoester, L. Serafini, and V. Zervaki, "Towards content-oriented patent document processing," World Patent Information, vol. 30, iss. 1, pp. 21-33, 2008.