Improving Interoperability Using Query Interpretation in Semantic Vector Spaces

  • Anthony Ventresque
  • Sylvie Cazalens
  • Philippe Lamarre
  • Patrick Valduriez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5021)


In semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the “shared” parts of the ontologies) but, in some sense, limited to them. Then, how the “unshared” parts can also contribute to and improve information exchange ? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator’s side and a query interpretation step at the document provider’s. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared.


Central Concept Query Expansion Interpretation Function Semantic Interoperability Ontology Match 


  1. 1.
    Berry, M.W., Drmac, Z., Jessup, E.R.: Matrices, vector spaces, and information retrieval. SIAM Rev. 41(2) (1999)Google Scholar
  2. 2.
    Bidault, A., Froidevaux, C., Safar, B.: Repairing queries in a mediator approach. In: ECAI (2000)Google Scholar
  3. 3.
    Desmontils, E., Jacquin, C.: Indexing a web site with a terminology oriented ontology. In: The Emerging Semantic Web (2002)Google Scholar
  4. 4.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (DE) (2007)MATHGoogle Scholar
  5. 5.
    Fellbaum, C.: WordNet: an electronic lexical database (1998)Google Scholar
  6. 6.
    Gaasterland, T., Godfrey, P., Minker, J.: An overview of cooperative answering. J. of Intelligent Information Systems 1(2), 123–157 (1992)CrossRefGoogle Scholar
  7. 7.
    Gómez-Pérez, A., Fernández, M., Corcho, O.: Ontological Engineering. Springer, London (2004)Google Scholar
  8. 8.
    Gonzalo, J., Verdejo, F., Chugur, I., Cigarran, J.: Indexing with wordnet synsets can improve text retrieval. In: COLING/ACL 1998 Workshop on Usage of WordNet for NLP (1998)Google Scholar
  9. 9.
    Ives, Z.G., Halevy, A.Y., Mork, P., Tatarinov, I.: Piazza: mediation and integration infrastructure for semantic web data. Journal of Web Semantics (2003)Google Scholar
  10. 10.
    Jiang, G., Cybenko, G., Kashyap, V., Hendler, J.A.: Semantic interoperability and information fluidity. Int. J. of cooperative Information Systems 15(1), 1–21 (2006)CrossRefGoogle Scholar
  11. 11.
    Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics. In: International Conference on Research in Computational Linguistics (1997)Google Scholar
  12. 12.
    Lin, D.: An information-theoretic definition of similarity. In: International Conf. on Machine Learning (1998)Google Scholar
  13. 13.
    Manning, C.D., Schtze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)MATHGoogle Scholar
  14. 14.
    Mena, E., Illaramendi, A., Kashyap, V., Sheth, A.: Observer: An approach for query processing in global information sytems based on interoperation across preexisting ontologies. Int. J. distributed and Parallel Databases 8(2), 223–271 (2000)CrossRefGoogle Scholar
  15. 15.
    Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Processes (1991)Google Scholar
  16. 16.
    Nie, J.-Y., Jin, F.: Integrating logical operators in query expansion invector space model. In: SIGIR workshop on Mathematical and Formal methods in Information Retrieval (2002)Google Scholar
  17. 17.
    Qiu, Y., Frei, H.P.: Concept based query expansion. In: SIGIR (1993)Google Scholar
  18. 18.
    Rousset, M.-C.: Small can be beautiful in the semantic web. In: International Semantic Web Conference, pp. 6–16 (2004)Google Scholar
  19. 19.
    Salton, G., MacGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)MATHGoogle Scholar
  20. 20.
    Sanderson, M.: Retrieving with good sense. Information Retrieval (2000)Google Scholar
  21. 21.
    Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in wordnet. In: ECAI (2004)Google Scholar
  22. 22.
    Tempich, C., Pinto, H.S., Staab, S.: Ontology engineering revisited: An iterative case study. In: ESWC, pp. 110–124 (2006)Google Scholar
  23. 23.
    Tversky, A.: Features of similarity. Psychological Review 84(4) (1977)Google Scholar
  24. 24.
    Voorhees, E.M.: Query expansion using lexical-semantic relations. In: SIGIR, Dublin (1994)Google Scholar
  25. 25.
    Woods, W.: Conceptual indexing: A better way to organize knowledge. Technical report, Sun Microsystems Laboratories (1997)Google Scholar
  26. 26.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: ACL (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Anthony Ventresque
    • 1
  • Sylvie Cazalens
    • 1
  • Philippe Lamarre
    • 1
  • Patrick Valduriez
    • 2
  1. 1.LINAUniversity of Nantes 
  2. 2.INRIA and LINAUniversity of Nantes 

Personalised recommendations