Learning Concept Mappings from Instance Similarity

  • Shenghui Wang
  • Gwenn Englebienne
  • Stefan Schlobach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5318)

Abstract

Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are actually being used. However such methods have not at present been widely investigated in ontology mapping, compared to linguistic and structural techniques. Furthermore, previous instance-based mapping techniques were only applicable to cases where a substantial set of instances was available that was doubly annotated with both vocabularies. In this paper we approach the mapping problem as a classification problem based on the similarity between instances of concepts. This has the advantage that no doubly annotated instances are required, so that the method can be applied to any two corpora annotated with their own vocabularies. We evaluate the resulting classifiers on two real-world use cases, one with homogeneous and one with heterogeneous instances. The results illustrate the efficiency and generality of this method.

References

  1. 1.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4) (2001)Google Scholar
  2. 2.
    Doan, A., Halevy, A.Y.: Semantic integration research in the database community: A brief survey. AI Magazine 26(1) (2005)Google Scholar
  3. 3.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)MATHGoogle Scholar
  4. 4.
    Li, W.S., Clifton, C., Liu, S.Y.: Database integration using neural networks: Implementation and experiences. Knowledge and Information Systems 2, 73–96 (2000)CrossRefMATHGoogle Scholar
  5. 5.
    Doan, A.H., Madhavan, J., Domingos, P., Halevy, A.: Learning to map between ontologies on the semantic web. In: Proceedings of the 11th international conference on World Wide Web, pp. 662–673 (2002)Google Scholar
  6. 6.
    Ichise, R., Takeda, H., Honiden, S.: Integrating multiple internet directories by instance-based learning. In: Proceedings of the eighteenth International Joint Conference on Artificial Intelligence (2003)Google Scholar
  7. 7.
    Isaac, A., van der Meij, L., Schlobach, S., Wang, S.: An empirical study of instance-based ontology matching. In: Proceedings of the 6th International Semantic Web Conference, Busan, Korea (2007)Google Scholar
  8. 8.
    Kindermann, R., Snell, J.L.: Markov Random Fields and their applications. American Mathematical Society (1980)Google Scholar
  9. 9.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning, pp. 282–289 (2001)Google Scholar
  10. 10.
    Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Edmonton, Canada, vol. 1, pp. 134–141. Association for Computational Linguistics (2003)Google Scholar
  11. 11.
    Nocedal, J.: Updating quasi-newton matrices with limited storage. Mathematics of Computation 35, 773–782 (1980)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Aleksovski, Z., ten Kate, W., van Harmelen, F.: Ontology matching using comprehensive ontology as background knowledge. In: Shvaiko, P., et al. (eds.) Proceedings of the International Workshop on Ontology Matching at ISWC 2006, CEUR, pp. 13–24 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shenghui Wang
    • 1
  • Gwenn Englebienne
    • 2
  • Stefan Schlobach
    • 1
  1. 1.Vrije Universiteit AmsterdamNetherlands
  2. 2.Universiteit van AmsterdamNetherlands

Personalised recommendations