ASeMatch: A Semantic Matching Method

  • Sandra Roger
  • Augustina Buccella
  • Alejandra Cechich
  • Manuel Sanz Palomar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4188)


Usually, syntactic information of different sources does not provide enough knowledge to discover possible matchings among them. Otherwise, more suitable matchings can be found by using the semantics of these sources. In this way, semantic matching involves the task of finding similarities among overlapping sources by using semantic knowledge. In the last years, the ontologies have emerged to represent this semantics. On these lines, we introduce our ASeMatch method for semantic matching. By applying several NLP tools and resources in a novel way and by using the semantic and syntactic information extracted from the ontologies, our method finds complex mappings such as 1–N and N–1 matchings.


Word Sense Disambiguation Syntactic Information Semantic Match Ontology Match Semantic Resource 
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.


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  1. 1.
    Le, B.T., Dieng-Kuntz, R., Gandon, F.: On ontology matching problems for building a corporate semantic web in a multi-communities organization. In: ICEIS 2004 Software Agents and Internet Computing, pp. 236–243 (2004)Google Scholar
  2. 2.
    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
  3. 3.
    Stephens, L., Gangam, A., Huhns, M.: Constructing Consensus Ontologies for the Semantic Web: A Conceptual Approach. In: World Wide Web: Internet and Web Information Systems, vol. 7, pp. 421–442. Kluwer Academic Publishers, Dordrecht (2004)Google Scholar
  4. 4.
    Richardson, R., Smeaton, A.: Using wordnet in a knowledge-based approach to information retrieval. Technical Report CA-0395, Dublin City Univ., School of Computer Applications, Dublin, Ireland (1995)Google Scholar
  5. 5.
    Buccella, A., Cechich, A., Brisaboa, N.R.: A federated layer to integrate heterogeneous knowledge. In: VODCA 2004 First Int. Workshop on Views on Designing Complex Architectures, Bertinoro, Italy. Electronic Notes in Theoretical Computer Science, pp. 101–118. Elsevier Science BV, Amsterdam (2004)Google Scholar
  6. 6.
    Buccella, A., Cechich, A., Brisaboa, N.R.: A three-level approach to ontology merging. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 80–89. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Magnini, B., Speranza, M., Girardi, G.: A semantic-based approach to interoperability of classification. hierarchies: Evaluation of linguistic techniques. In: Proceeding of COLING 2004, Geneva, Switzerland (2004)Google Scholar
  8. 8.
    Rodríguez, M.A., Egenhofer, M.J.: Determining semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering 15, 442–456 (2003)CrossRefGoogle Scholar
  9. 9.
    Tversky, A.: Features of similarity. Psychological Review 84, 327–352 (1977)CrossRefGoogle Scholar
  10. 10.
    Dieng, R., Hug, S.: Comparison of personal ontologies represented through conceptual graphs. In: Proceedings of the ECAI 1998 – 13th European Conference on Artificial Intelligent, Brigthon, UK, pp. 341–345 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sandra Roger
    • 1
    • 2
  • Augustina Buccella
    • 2
  • Alejandra Cechich
    • 2
  • Manuel Sanz Palomar
    • 1
  1. 1.Natural Language Processing and Information Systems Group, Department of Software and Computing SystemsUniversity of AlicanteSpain
  2. 2.GIISCO Research Group, Department of Computing SciencesUniversity of ComahueArgentina

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