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CSR: Discovering Subsumption Relations for the Alignment of Ontologies

  • Vassilis Spiliopoulos
  • Alexandros G. Valarakos
  • George A. Vouros
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5021)

Abstract

For the effective alignment of ontologies, the computation of equivalence relations between elements of ontologies is not enough: Subsumption relations play a crucial role as well. In this paper we propose the "Classification-Based Learning of Subsumption Relations for the Alignment of Ontologies" (CSR) method. Given a pair of concepts from two ontologies, the objective of CSR is to identify patterns of concepts’ features that provide evidence for the subsumption relation among them. This is achieved by means of a classification task, using state of the art supervised machine learning methods. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series and discusses the potential of the method.

Keywords

ontology alignment subsumption supervised machine learning 

References

  1. 1.
    Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 14–171. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Svab, O., Svatek, V., Stuckenschmidt, H.: A Study in Empirical and ’Casuistic’ Analysis of Ontology Mapping Results. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, Springer, Heidelberg (2007)Google Scholar
  3. 3.
    Mitchell, T.: Machine Learning. The McGraw-Hill Companies, Inc, New York (1997)zbMATHGoogle Scholar
  4. 4.
    Giunchiglia, F., Yatskevich, M., Shvaiko, P.: Semantic Matching: Algorithms and implementation. Journal on Data Semantics IX (2007)Google Scholar
  5. 5.
    Bouquet, P., Serafini, L., Zanobini, S., Sceffer, S.: Bootstrapping semantics on the web: meaning elicitation from schemas. In: WWW, Edinburgh, Scotland (2006)Google Scholar
  6. 6.
    Aleksovski, Z., Klein, M., Kate, W., Harmelen, F.: Matching Unstructured Vocabularies Using a Background Ontology. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Gracia, J., Lopez, V., D’Aquin, M., Sabou, M., Motta, E., Mena, E.: Solving Semantic Ambiguity to Improve Semantic Web based Ontology Matching. In: Ontology Matching Workshop, Busan, Korea (2007)Google Scholar
  8. 8.
    Risto, G., Zharko, A., Warner, K.: Using Google Distance to weight approximate ontology matches. In: WWW, Banff, Alberta, Canada (2007)Google Scholar
  9. 9.
    Van Hage, W.R., Katrenko, S., Schreiber, A.T.: A Method to Combine Linguistic Ontology Mapping Techniques. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Cimiano, P., Staab, S.: Learning by googling. In: SIGKDD Explor., Newsl., USA (2004)Google Scholar
  11. 11.
    Jerome, D., Fabrice, G., Regis, G., Henri, B.: An interactive, asymmetric and extensional method for matching conceptual hierarchies. In: EMOI – INTEROP Workshop, Luxembourg (2006)Google Scholar
  12. 12.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)Google Scholar
  13. 13.
    Spiliopoulos, V., Valarakos, A.G., Vouros, G.A., Karkaletsis, V.: SEMA: Results for the ontology alignment contest OAEI 2007. In: Ontology Matching Workshop, OAEI, Busan, Korea (2007)Google Scholar
  14. 14.
    Ontology Alignment Evaluation Initiative, http://oaei.ontologymatching.org/
  15. 15.
    Japkowicz, N.: The Class Imbalance Problem: Significance and Strategies. In: ICAI, Special Track on Inductive Learning, Las Vegas, Nevada (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vassilis Spiliopoulos
    • 1
    • 2
  • Alexandros G. Valarakos
    • 1
  • George A. Vouros
    • 1
  1. 1.AI Lab, Information and Communication Systems Engineering DepartmentUniversity of the AegeanSamosGreece
  2. 2.Institution of Informatics and TelecommunicationsNCSR ”Demokritos”Greece

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