Advertisement

Block Matching for Ontologies

  • Wei Hu
  • Yuzhong Qu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4273)

Abstract

Ontology matching is a crucial task to enable interoperation between Web applications using different but related ontologies. Today, most of the ontology matching techniques are targeted to find 1:1 mappings. However, block mappings are in fact more pervasive. In this paper, we discuss the block matching problem and suggest that both the mapping quality and the partitioning quality should be considered in block matching. We propose a novel partitioning-based approach to address the block matching issue. It considers both linguistic and structural characteristics of domain entities based on virtual documents, and uses a hierarchical bisection algorithm for partitioning. We set up two kinds of metrics to evaluate of the quality of block matching. The experimental results demonstrate that our approach is feasible.

Keywords

Resource Description Framework Mapping Quality Local Description Vector Space Model Block Match 
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.

References

  1. 1.
    Castano, S., De Antonellis, V., De Capitani Di Vimercati, S.: Global viewing of heterogeneous data sources. IEEE Transactions on Knowledge and Data Engineering 13(2), 277–297 (2001)CrossRefGoogle Scholar
  2. 2.
    Cheng, D., Kannan, R., Vempala, S., Wang, G.: A divide-and-merge methodology for clustering. In: Proceedings of the 24th ACM Symposium on Principles of Database Systems (PODS 2005), pp. 196–205 (2005)Google Scholar
  3. 3.
    Cuenca Grau, B., Parsia, B., Sirin, E.: Combining OWL ontologies using ε-connections. Journal of Web Semantics 4(1) (2005)Google Scholar
  4. 4.
    Dhamankar, R., Lee, Y., Doan, A.H., Halevy, A., Domingos, P.: iMAP: Discovering complex semantic matches between database schemas. In: Proceedings of the 23th ACM International Conference on Management of Data (SIGMOD 2004), pp. 383–394 (2004)Google Scholar
  5. 5.
    Ding, C.H.Q., He, X., Zha, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM 2001), pp. 107–114 (2001)Google Scholar
  6. 6.
    Doan, A., Madhavan, J., Dhamankar, R., Domingos, P., Halevy, A.Y.: Learning to match ontologies on the semantic web. VLDB Journal 12(4), 303–319 (2003)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-Lite. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 333–337 (2004)Google Scholar
  9. 9.
    Fiedler, M.: A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory. Czechoslovak Mathematical Journal 25, 619–633 (1975)MathSciNetGoogle Scholar
  10. 10.
    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
  11. 11.
    Hu, W., Zhao, Y.Y., Qu, Y.Z.: Partition-based block matching of large class hierarchies. In: Mizoguchi, R., Shi, Z.-Z., Giunchiglia, F. (eds.) ASWC 2006. LNCS, vol. 4185, pp. 72–83. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Klyne, G., Carroll, J.J. (eds.): Resource description framework (RDF): Concepts and abstract syntax. W3C Recommendation (February 10, 2004), latest version is available at: http://www.w3.org/TR/rdf-concepts/
  13. 13.
    Kotis, K., Vouros, G.A., Stergiou, K.: Towards automatic merging of domain ontologies: The HCONE-merge approach. Journal of Web Semantics 4(1) (2005)Google Scholar
  14. 14.
    Noy, N.F., Musen, M.A.: The PROMPT suite: Interactive tools for ontology merging and mapping. International Journal of Human-Computer Studies 59, 983–1024 (2003)CrossRefGoogle Scholar
  15. 15.
    Noy, N.F., Musen, M.A.: Specifying ontology views by traversal. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 713–725. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Parlett, B.N.: The symmetric eigenvalue problem. SIAM, Philadelphia (1998)MATHGoogle Scholar
  17. 17.
    Raghavan, V.V., Wong, S.K.M.: A critical analysis of vector space model for information retrieval. Journal of the American Society for Information Science 37(5), 279–287 (1986)Google Scholar
  18. 18.
    Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. VLDB Journal 10, 334–350 (2001)MATHCrossRefGoogle Scholar
  19. 19.
    Patel-Schneider, P.F., Hayes, P., Horrocks, I. (eds.): OWL web ontology language semantics and abstract syntax. W3C Recommendation (February 10, 2004), latest version is available at: http://www.w3.org/TR/owl-semantics/
  20. 20.
    Qu, Y.Z., Hu, W., Cheng, G.: Constructing virtual documents for ontology matching. In: Proceedings of the 15th International World Wide Web Conference (WWW 2006), pp. 23–31 (2006)Google Scholar
  21. 21.
    Salton, G., McGill, M.H.: Introduction to modern information retrieval. McGraw-Hill, New York (1983)MATHGoogle Scholar
  22. 22.
    Seidenberg, J., Rector, A.: Web ontology segmentation: analysis, classification and use. In: Proceedings of the 15th International World Wide Web Conference (WWW 2006), pp. 13–22 (2006)Google Scholar
  23. 23.
    Stoilos, G., Stamou, G., Kollias, S.: A string metric for ontology alignment. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 623–637. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Stuckenschmidt, H., Klein, M.: Structure-based partitioning of large concept hierarchies. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 289–303. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Hu
    • 1
  • Yuzhong Qu
    • 1
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingP.R. China

Personalised recommendations