Semantic Relatedness Measure Using Object Properties in an Ontology

  • Laurent Mazuel
  • Nicolas Sabouret
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5318)


This paper presents a new semantic relatedness measure on ontologies which considers especially the object properties between the concepts. Our approach relies on two hypotheses. Firstly, using only concept hierarchy and object properties, only a few paths can be considered as “semantically corrects” and these paths obey to a given set of rules. Secondly, following a given edge in a path has a cost (represented as a weight), which depends on its type (\(is\mbox{-}a\), \(part\mbox{-}of\), etc.), its context in the ontology and its position in this path. We propose an evaluation of our measure on the lexical base WordNet using \(part\mbox{-}of\) relation with two different benchmarks. We show that, in this context, our measure outperforms the classical semantic measures.


Semantic Similarity Object Property Semantic Distance Dialogue System Concept Hierarchy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: 14th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 448–453 (1995)Google Scholar
  2. 2.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and Application of a Metric on Semantic Nets. IEEE Transactions on Systems, Man, and Cybernetics 19, 17–30 (1989)CrossRefGoogle Scholar
  3. 3.
    Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of semantic distance. Computational Linguistics 32, 13–47 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Patwardhan, S., Pedersen, T.: Using WordNet Based Context Vectors to Estimate the Semantic Relatedness of Concepts. In: Proc. of the EACL 2006 Workshop Making Sense of Sense - Bringing Computational Linguistics and Psycholinguistics Together (2006)Google Scholar
  5. 5.
    Strube, M., Ponzetto, S.: WikiRelate! Computing semantic relatedness using Wikipedia. In: Proc. of AAAI, vol. 6, pp. 1419–1424 (2006)Google Scholar
  6. 6.
    Cilibrasi, R., Vitanyi, P.: Automatic Extraction of Meaning from the Web. In: Proc. IEEE International Symposium on Information Theory, pp. 2309–2313 (2006)Google Scholar
  7. 7.
    Iosif, E., Potamianos, A.: Unsupervised Semantic Similarity Computation usingWeb Search Engines. In: International Conference on Web Intelligence (WI), pp. 381–387. IEEE, Los Alamitos (2007)Google Scholar
  8. 8.
    Hirst, G., St-Onge, D.: Lexical chains as representation of context for the detection and correction malapropisms. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 305–332. MIT Press, Cambridge (1998)Google Scholar
  9. 9.
    Corby, O., Dieng-Kuntz, R., Faron-Zucker, C.: Querying the Semantic Web with the CORESE search engine. In: Press, I. (ed.) Proc. of the ECAI 2004, Valencia, pp. 705–709 (2004)Google Scholar
  10. 10.
    Milward, D., Beveridge, M.: Ontology-based dialogue systems. In: Proc. 3rd Workshop on Knowledge and reasoning in practical dialogue systems (IJCAI 2003), pp. 9–18 (August 2003)Google Scholar
  11. 11.
    Dzikovska, M.O., Allen, J.F., Swift, M.D.: Integrating linguistic and domain knowledge for spoken dialogue systems in multiple domains. In: Proc. of IJCAI 2003 Workshop on Knowledge and Reasoning in Practical Dialogue Systems (2003)Google Scholar
  12. 12.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (DE) (2007)zbMATHGoogle Scholar
  13. 13.
    Maedche, A., Staab, S.: Measuring similarity between ontologies. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 251–263. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Eliasson, K.: Case-Based Techniques Used for Dialogue Understanding and Planning in a Human-Robot Dialogue System. In: Proc. of IJCAI 2007, pp. 1600–1605 (2007)Google Scholar
  15. 15.
    Hau, J., Lee, W., Darlington, J.: A Semantic Similarity Measure for Semantic Web Services. In: Proc. Workshop on Web Service Semantics (2005)Google Scholar
  16. 16.
    Smith, M.K., Welty, C., McGuinness, D.L.: Owl web ontology language guide (February 2004),
  17. 17.
    Turney, P.: Similarity of Semantic Relations. Computational Linguistics 32(3), 379–416 (2006)CrossRefzbMATHGoogle Scholar
  18. 18.
    Sussna, M.: Word Sense Disambiguation for Free-text Indexing Using a Massive Semantic Network. In: Bhargava, B.K., Finin, T.W., Yesha, Y. (eds.) Proc. of the 2nd International Conference on Information and Knowledge Management (CIKM 1993), Washington, DC, USA, November, pp. 67–74. ACM, New York (1993)Google Scholar
  19. 19.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd. Annual Meeting of the Association for Computational Linguistics, New Mexico State University, Las Cruces, New Mexico, pp. 133–138 (1994)Google Scholar
  20. 20.
    Zhong, J., Zhu, H., Li, J., Yu, Y.: Conceptual graph matching for semantic search. In: ICCS 2002. Proceedings of the 10th International Conference on Conceptual Structures, London, UK, pp. 92–196. Springer, Heidelberg (2002)Google Scholar
  21. 21.
    Seco, N., Veale, T., Hayes, J.: An Intrinsic Information Content Metric for Semantic Similarity in WordNet. In: Proc. ECAI 2004, the 16th European Conference on Artificial Intelligence, pp. 1089–1090 (2004)Google Scholar
  22. 22.
    Lin, D.: An information-theoretic definition of similarity. In: Proc. 15th International Conf. on Machine Learning, pp. 296–304. Morgan Kaufmann, San Francisco, CA (1998)Google Scholar
  23. 23.
    Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proc. on International Conference on Research in Computational Linguistics, Taiwan, pp. 19–33 (1997)Google Scholar
  24. 24.
    Aleksovski, Z., ten Kate, W., van Harmelen, F.: Exploiting the structure of background knowledge used in ontology matching. In: Proc. Workshop on Ontology Matching in ISWC 2006, CEUR Workshop Proceedings (2006)Google Scholar
  25. 25.
    Fellbaum, C. (ed.): WordNet, An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  26. 26.
    Miller, G., Charles, W.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1), 1–28 (1991)CrossRefGoogle Scholar
  27. 27.
    Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: the concept revisited. In: WWW 2001. Proceedings of the 10th international conference on World Wide Web, pp. 406–414. ACM Press, New York (2001)Google Scholar
  28. 28.
    Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman Publishing Co., Inc., Boston, MA (1999)Google Scholar
  29. 29.
    Mazuel, L., Sabouret, N.: Generic command interpretation algorithms for conversational agents. Web Intelligence and Agent Systems  6(2) (April 2008)Google Scholar
  30. 30.
    Sutton, R.: Learning to predict by the methods of temporal differences. Machine Learning 3(1), 9–44 (1988)Google Scholar
  31. 31.
    Watkins, C., Dayan, P.: Q-learning. Machine Learning 8(3), 279–292 (1992)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Laurent Mazuel
    • 1
  • Nicolas Sabouret
    • 1
  1. 1.Laboratoire Informatique de Paris 6 - LIP6ParisFrance

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