Evaluation of Similarity Measures for Ontology Mapping

  • Ryutaro Ichise
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5447)


This paper presents an analysis of similarity measures for identifying ontology mapping. Using discriminant analysis, we investigated forty-eight similarity measures such as string matching and knowledge based similarities that have been used in previous systems. As a result, we extracted twenty-two effective similarity measures for identifying ontology mapping out of forty-eight possible similarity measures. The extracted measures vary widely in the type in similarity.


Similarity Measure Word List Edit Distance String Match Word Similarity 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ichise, R.: Machine learning approach for ontology mapping using multiple concept similarity measures. In: Proceedings of the 7th IEEE/ACIS International Conference on Computer and Information Science, pp. 340–346 (2008)Google Scholar
  2. 2.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  3. 3.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: Proceedings of the 18th International Conference on Data Engineering, San Jose, CA, pp. 117–128 (February 2002)Google Scholar
  4. 4.
    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
  5. 5.
    Fellbaum, C.: Wordnet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  6. 6.
    Aumueller, D., Do, H.H., Massmann, S., Rahm, E.: Schema and ontology matching with COMA++. In: Özcan, F. (ed.) Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 906–908. ACM, New York (2005)Google Scholar
  7. 7.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, New Mexico State University, Las Cruces, New, Mexico, pp. 133–138 (1994)Google Scholar
  8. 8.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296–304. Morgan Kaufmann, San Francisco (1998)Google Scholar
  9. 9.
    Ichise, R., Takeda, H., Honiden, S.: Integrating multiple internet directories by instance-based learning. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 22–28 (2003)Google Scholar
  10. 10.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: similarity - measuring the relatedness of concepts. In: Proceedings of the 19th National Conference on Artificial Intelligence, pp. 1024–1025 (2004)Google Scholar
  11. 11.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ryutaro Ichise
    • 1
  1. 1.National Institute of InformaticsTokyoJapan

Personalised recommendations