Advertisement

Efficient Semantic Matching

  • Fausto Giunchiglia
  • Mikalai Yatskevich
  • Enrico Giunchiglia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3532)

Abstract

We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into propositional formulas, and by codifying matching into a propositional unsatisfiability problem. We distinguish between problems with conjunctive formulas and problems with disjunctive formulas, and present various optimizations. For instance, we propose a linear time algorithm which solves the first class of problems. According to the tests we have done so far, the optimizations substantially improve the time performance of the system.

Keywords

Semantic Relation Conjunctive Normal Form Propositional Formula Unit Clause Semantic 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.
    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
  2. 2.
    Davis, M., Putnam, H.: A computing procedure for quantification theory. Journal of the ACM 7, 201–215 (1960)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Do, H., Rahm, E.: COMA - A system for Flexible Combination of Schema Matching Approaches. In: Proceedings of VLDB 2002 (2002)Google Scholar
  4. 4.
    Giunchiglia, E., Sebastiani, R.: Applying the Davis-Putnam procedure to non-clausal formulas. In: AIIA 1999 (1999)Google Scholar
  5. 5.
    Giunchiglia, F., Shvaiko, P.: Semantic Matching. In The Knowledge Engineering Review Journal 18(3) (2003)Google Scholar
  6. 6.
    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
  7. 7.
    Giunchiglia, F., Yatskevich, M.: Element level semantic matching. In: Proceedings of Meaning Coordination and Negotiation workshop at ISWC (2004)Google Scholar
  8. 8.
    Le Berre, D.: JSAT: The java satisfiability library, http://cafe.newcastle.edu.au/daniel/JSAT/
  9. 9.
    Le Berre, D.: SAT4J: A satisfiability library for Java, http://www.sat4j.org/
  10. 10.
    Madhavan, J., Bernstein, P., Rahm, E.: Generic Schema Matching with Cupid. In: VLDB 2001 (2001)Google Scholar
  11. 11.
    Magnini, B., Speranza, M., Girardi, C.: A Semantic-based Approach to Interoperability of classification Hierarchies: Evaluation of Linguistic Techniques. In: Proceedings of COLING 2004, Geneva, Switzerland, August 23-27 (2004)Google Scholar
  12. 12.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity Flooding: A Versatile Graph Matching Algorithm. In: Proceedings of ICDE, pp. 117-128 (2002)Google Scholar
  13. 13.
    Melnik, S., Rahm, E., Bernstein, P.: Rondo: A programming platform for generic model management. In: Proceedings of SIGMOD 2003, pp. 193–204 (2003)Google Scholar
  14. 14.
    Plaisted, D., Greenbaum, S.: A Structure-preserving Clause Form Translation. Journal of Symbolic Computation 2, 293–304 (1986)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Tsetin, G.: On the complexity proofs in propositional logics. Seminars in Mathematics, 8 (1970)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Fausto Giunchiglia
    • 1
  • Mikalai Yatskevich
    • 1
  • Enrico Giunchiglia
    • 2
  1. 1.Dept. of Information and Communication TechnologyUniversity of TrentoPovo, TrentoItaly
  2. 2.DISTUniversita di GenovaGenovaItaly

Personalised recommendations