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Using the Intension of Classes and Properties Definition in Ontologies for Word Sense Disambiguation

  • Khaled Khelif
  • Fabien Gandon
  • Olivier Corby
  • Rose Dieng-Kuntz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)

Abstract

We present an ontology-driven word sense disambiguation process. The main idea consists of using the context of the ambiguous word to decide which class can be assigned to it. The disambiguation relies on similarities between classes assigned to the ambiguous word, classes assigned to terms close to it in the text, and on the type of properties that could occur between them. The computation of the similarity uses domain ontologies to provide semantic distances based on definitions in intension. We tested our approach in the extraction of annotations from biomedical texts.

Keywords

word sense disambiguation ontology semantic distances 

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References

  1. 1.
    Corby, O., Dieng-Kuntz, R., Faron-Zucker, C., Gandon, F.: Searching the Semantic Web: Approximate Query Processing Based on Ontologies. IEEE Intelligent Systems 21(1), 1541–1672 (2006)CrossRefGoogle Scholar
  2. 2.
    Gandon, F.: Distributed Artificial Intelligence and Knowledge Management: ontologies and multi-agent systems for a corporate semantic web, Ph.D Thesis. INRIA (2002)Google Scholar
  3. 3.
    Gandon, F., Corby, O., Giboin, A., Gronnier, N., Guigard, C.: Graph-based inferences in a Semantic Web Server for the Cartography of Competencies in a Telecom Valley. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 247–261. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Budanitsky, A., Hirst, G.: Semantic distance in WordNet: An Experimental, Application-oriented Evaluation of five Measures. In: Workshop on WordNet and Other Lexical Resources, Second meeting of the North American Chapter of the Association for Computational Linguistics, Pittsburgh, PA (2001)Google Scholar
  5. 5.
    Collins, A., Loftus, E.: A Spreading Activation Theory of Semantic Processing. Psychological Review 82, 407–428 (1975)CrossRefGoogle Scholar
  6. 6.
    Jiang, J., Conrath, D.: Semantic Similarity based on Corpus Statistics and Lexical Taxonomy. In: Proc. of International Conference on Research in Computational Linguistics (1997)Google Scholar
  7. 7.
    Quillian, M.: Semantic Memory. In: Minsky, M. (ed.) Semantic Information Processing, pp. 227–270. MIT Press, Cambridge; Readings in Cognitive Science, section 2.1 Google Scholar
  8. 8.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and Application of a Metric on Semantic Nets. IEEE Transaction on Systems, Man, and Cybernetics, 17–30 (1989)Google Scholar
  9. 9.
    Resnik, P.: Semantic Similarity in a Taxonomy: An Information-Based Measure and its Applications to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research 11, 95–130 (1995)Google Scholar
  10. 10.
    Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: Proc. of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico (1994)Google Scholar
  11. 11.
    Khelif, K., Dieng-Kuntz, R., Barbry, P.: An ontology-based approach to support text mining and information retrieval in the biological domain. Journal of Universal Computer Science (JUCS) 13(12), 1881–1907 (2007)Google Scholar
  12. 12.
    Weeber, M., Mork, J., Aronson, A.: Developing a test collection for biomedical word sense disambiguation (2001)Google Scholar
  13. 13.
    Humphrey, S.M., Rogers, W.J., Kilicoglu, H., Demner-Fushman, D., Rindflesch, T.C.: Word sense disambiguation by selecting the best semantic type based on journal descriptor indexing: Preliminary experiment. J. Am. Soc. Inf. Sci. Technol., 96–113 (2006)Google Scholar
  14. 14.
    Humphreys, B., Lindberg, D.: The umls project: making the conceptual connection between users and the information they need. Bull. Med. Libr. Assoc., 170–177 (1993)Google Scholar
  15. 15.
    Liu, H., Johnson, S., Friedman, C.: Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the umls. J. Am. Med. Inform. Assoc. (2002)Google Scholar
  16. 16.
    Edmonds, P., Kilgarriff, A.: Introduction to the special issue on evaluating word sense disambiguation systems. Nat. Lang. Eng. 8(4), 279–291 (2002)CrossRefGoogle Scholar
  17. 17.
    Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proc. AMIA Symp., pp. 17–21 (2001)Google Scholar
  18. 18.
    Pedersen, T., Banerjee, S., Patwardhan, S.: Maximizing Semantic Relatedness to Perform Word Sense Disambiguation, University of Minnesota, Research Report UMSI (2005)/25Google Scholar
  19. 19.
    Hassell, J., Aleman-Meza, B., Arpinar, I.B.: Ontology-driven automatic entity disambiguation in unstructured text. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 44–57. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Corby, O.: Web, Graphs & Semantics. In: Proc. of the 16th International Conference on Conceptual Structures (ICCS 2008), Toulouse, France (2008)Google Scholar
  21. 21.
    Widdows D., Peters S., Cederberg S., Chan C., Steffen D., Buitelaar P.: Unsupervised monolingual and bilingual word-sense disambiguation of medical documents using UMLS. In: Natural Language Processing in Biomedicine, ACL 2003 Workshop, Sapporo (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Khaled Khelif
    • 1
  • Fabien Gandon
    • 1
  • Olivier Corby
    • 1
  • Rose Dieng-Kuntz
    • 1
  1. 1.INRIA Sophia Antipolis MéditerranéeSophiaFrance

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