Web Explanations for Semantic Heterogeneity Discovery

  • Pavel Shvaiko
  • Fausto Giunchiglia
  • Paulo Pinheiro da Silva
  • Deborah L. McGuinness
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3532)

Abstract

Managing semantic heterogeneity is a complex task. One solution involves matching like terms to each other. We view Match as an operator that takes two graph-like structures (e.g., concept hierarchies or ontologies) and returns a mapping between the nodes of the graphs that correspond semantically to each other. While some state of the art matching systems may produce effective mappings, these mappings may not be intuitively obvious to human users. In order for users to trust the mappings, and thus, use them, they need information about them (e.g., they need access to the sources that were used to determine semantic correspondences between terms). In this paper we describe how a matching system can explain its answers using the Inference Web (IW) infrastructure thus making the matching process transparent. The proposed solution is based on the assumption that mappings are computed by logical reasoning. There, S-Match, a semantic matching system, produces proofs and explanations for mappings it has discovered.

Keywords

Europe Lution Extractor 

References

  1. 1.
    Barrett, C., Berezin, S.: A proof-producing boolean search engine. In: Proceedings of PDPAR (2003)Google Scholar
  2. 2.
    Le Berre, D.: JSAT: The java satisfiability library (2001), http://cafe.newcastle.edu.au/daniel/JSAT/
  3. 3.
    Borgida, A., Franconi, E., Horrocks, I., McGuinness, D., Patel-Schneider, P.: Explaining ALC subsumption. In: Proceedings of Description Logics workshop (1999)Google Scholar
  4. 4.
    Bouquet, P., Serafini, L., Zanobini, S.: Semantic coordination: A new approach and an application. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 130–145. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    da Silva, P.P., McGuinness, D.L., Fikes, R.: A proof markup language for semantic web services. Technical report, KSL, Stanford University (2004)Google Scholar
  6. 6.
    Davis, M., Longemann, G., Loveland, D.: A machine program for theorem proving. Journal of the ACM 5(7) (1962)Google Scholar
  7. 7.
    Davis, M., Putnam, H.: A computing procedure for quantification theory. Journal of the ACM (7), 201–215 (1960)Google Scholar
  8. 8.
    Dhamankar, R., Lee, Y., Doan, A., Halevy, A., Domingos, P.: iMAP: Discovering complex semantic matches between database schemas. In: Proceedings of SIGMOD, pp. 383–394 (2004)Google Scholar
  9. 9.
    Ehrig, M., Staab, S.: QOM: Quick ontology mapping. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 683–697. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-lite. In: Proceedings of ECAI, pp. 333–337 (2004)Google Scholar
  11. 11.
    Giunchiglia, F., Shvaiko, P.: Semantic matching. KER Journal 18(3), 265–280 (2003)Google Scholar
  12. 12.
    Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-Match: an algorithm and an implementation of semantic matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61–75. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Giunchiglia, F., Yatskevich, M.: Element level semantic matching. In: Proceedings of Meaning Coordination and Negotiation workshop at ISWC (2004)Google Scholar
  14. 14.
    Giunchiglia, F., Yatskevich, M., Giunchiglia, E.: Efficient semantic matching. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 272–289. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Goldberg, E., Novikov, Y.: Verication of proofs of unsatisability for CNF formulas. In: Proceedings of DATE (2003)Google Scholar
  16. 16.
    Guarino, N.: The role of ontologies for the Semantic Web (and beyond). Technical report, Laboratory for Applied Ontology, Institute for Cognitive Sciences and Technology, ISTC-CNR (2004)Google Scholar
  17. 17.
    Do, H.H., Rahm, E.: COMA - a system for flexible combination of schema matching approaches. In: Proceedings of VLDB, pp. 610–621 (2001)Google Scholar
  18. 18.
    Horrocks, I., Patel-Schneider, P.F.: FaCT and DLP. In: de Swart, H. (ed.) TABLEAUX 1998. LNCS (LNAI), vol. 1397, pp. 27–30. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  19. 19.
    Madhavan, J., Bernstein, P., Rahm, E.: Generic schema matching with Cupid. In: Proceedings of VLDB, pp. 49–58 (2001)Google Scholar
  20. 20.
    Magnini, B., Serafini, L., Speranza, M.: Making explicit the semantics hidden in schema models. In: Proceedings of workshop on Human Language Technology for the Semantic Web and Web Services at ISWC (2003)Google Scholar
  21. 21.
    McGuinness, D.L., Borgida, A.: Explaining subsumption in description logics. In: Proceedings of IJCAI, pp. 816–821 (1995)Google Scholar
  22. 22.
    McGuinness, D.L., Pinheiro da Silva, P.: Infrastructure for web explanations. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 113–129. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  23. 23.
    McGuinness, D.L., da Silva, P.P.: Registry-based support for information integration. In: Proceedings of IJCAI Workshop on Information Integration on the Web (2003)Google Scholar
  24. 24.
    Melnik, S., Rahm, E., Bernstein, P.: Rondo: A programming platform for generic model management. In: Proceedings of SIGMOD, pp. 193–204 (2003)Google Scholar
  25. 25.
    Miller, A.G.: WordNet: A lexical database for english. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  26. 26.
    Moskewicz, M., Madigan, C., Zhaod, Y., Zhang, L., Malik, S.: Chaff: Engineering an efficient SAT solver. In: Proceedings of DAC (2001)Google Scholar
  27. 27.
    Noy, N., Musen, M.A.: Anchor-prompt: Using non-local context for semantic matching. In: Procedings of IJCAI workshop on Ontologies and Information Sharing, pp. 63–70 (2001)Google Scholar
  28. 28.
    Oh, Y., Mneimneh, M.N., Andraus, Z.S., Sakallah, K.A., Markov, I.L.: AMUSE: A minimally-unsatisfiable subformula extractor. In: Proceedings of DAC, pp. 518–523 (2004)Google Scholar
  29. 29.
    Parsia, B., Sirin, E., Grove, M., Alford, R.: Pellet OWL reasoner, http://www.mindswap.org/2003/pellet/index.shtml
  30. 30.
    Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. VLDB Journal 10(4), 334–350 (2001)MATHCrossRefGoogle Scholar
  31. 31.
    Shvaiko, P.: A classification of schema-based matching approaches. In: Proceedings of Meaning Coordination and Negotiation workshop at ISWC (2004)Google Scholar
  32. 32.
    Shvaiko, P., Euzenat, J.: A survey of schema-based macthing approaches. Technical report, DIT-04-087, University of Trento (2004)Google Scholar
  33. 33.
    Smith, M.K., Welty, C., McGuinness, D.L.: OWL web ontology language guide. Technical report, World Wide Web Consortium (W3C) February 10 (2004), http://www.w3.org/TR/2004/REC-owl-guide-20040210/
  34. 34.
    Wache, H., Voegele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Huebner, S.: Ontology-based integration of information - a survey of existing approaches. In: Proceedings of IJCAI workshop on Ontologies and Information Sharing, pp. 108–117 (2001)Google Scholar
  35. 35.
    Zhang, L., Malik, S.: Extracting small unsatisfiable cores from unsatisfiable boolean formulas. In: Proceedings of SAT (2003)Google Scholar
  36. 36.
    Zhang, L., Malik, S.: Validating SAT solvers using an independent resolution-based checker: Practical implementations and other applications. In: Proceedings of DATE (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pavel Shvaiko
    • 1
  • Fausto Giunchiglia
    • 1
  • Paulo Pinheiro da Silva
    • 2
  • Deborah L. McGuinness
    • 2
  1. 1.University of TrentoPovo,TrentoItaly
  2. 2.Stanford UniversityStanfordUSA

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