LCS: A Linguistic Combination System for Ontology Matching

  • Qiu Ji
  • Weiru Liu
  • Guilin Qi
  • David A. Bell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4092)


Ontology matching is an essential operation in many application domains, such as the Semantic Web, ontology merging or integration. So far, quite a few ontology matching approaches or matchers have been proposed. It has been observed that combining the results of multiple matchers is a promising technique to get better results than just using one matcher at a time. Many aggregation operators, such as Max, Min, Average and Weighted, have been developed. The limitations of these operators are studied. To overcome the limitations and provide a semantic interpretation for each aggregation operator, in this paper, we propose a linguistic combination system (LCS), where a linguistic aggregation operator (LAO), based on the ordered weighted averaging (OWA) operator, is used for the aggregation. A weight here is not associated with a specific matcher but a particular ordered position. A large number of LAOs can be developed for different uses, and the existing aggregation operators Max, Min and Average are the special cases in LAOs. For each LAO, there is a corresponding semantic interpretation. The experiments show the strength of our system.


Aggregation Operator Semantic Interpretation Ordered Weighted Average Ordered Weighted Average Operator Ontology 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.


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  1. 1.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284(5), 34–43 (2001)CrossRefGoogle Scholar
  2. 2.
    Do, H., Rahm, E.: COMA - a system for flexible combination of schema matching approaches. In: Proceedings of the 28th VLDB Conference, pp. 610–621 (2002)Google Scholar
  3. 3.
    Doan, A., Domingos, P., Halevy, A.Y.: Reconciling schemas of disparate data sources: a machine-learning approach. SIGMOD Record (ACM Special Interest Group on Management of Data), pp. 509–520 (2001)Google Scholar
  4. 4.
    Ehrig, M., Sure, Y.: Ontology mapping - an integrated approach. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 76–91. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. In: Proceedings of the Twenty-seventh International Conference on Very Large Data Bases(VLDB), Roma, Italy, September 11-14, 2001, pp. 49–58. Morgan Kaufmann, Los Altos (2001)Google Scholar
  6. 6.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: Proceedings of Eighteenth International Conference on Data Engineering, San Jose, California (2002)Google Scholar
  7. 7.
    Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-Lite. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI), Valencia, Spain, pp. 333–337 (2004)Google Scholar
  8. 8.
    Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. The International Journal on Very Large Data Bases (VLDB) 10(4), 334–350 (2001)MATHCrossRefGoogle Scholar
  9. 9.
    Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Tu, K., Yu, Y.: CMC: Combining multiple schema-matching strategies based on credibility prediction. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 17–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Yager, R.R.: On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. on Systems, Man and Cybernetics 18, 183–190 (1988)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Xu, Z.: An overview of methods for determining OWA weights. International Journal of Intelligent Systems 20(8), 843–865 (2005)MATHCrossRefGoogle Scholar
  13. 13.
    Yager, R.R.: Family of OWA operators. Fuzzy Sets and Systems 59, 125–148 (1993)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Yatskevich, M.: Preliminary evaluation of schema matching systems. Technical Report # DIT-03-028, Department of Information and Communication Technology, University Of Trento (Italy) (2003)Google Scholar
  15. 15.
    Yager, R.R., Kacprzyk, J.: The Ordered Weighted Averaging Operation: Theory, Methodology and Applications, pp. 167–178. Kluwer Academic Publishers, Boston (1997)Google Scholar
  16. 16.
    O’Hagan, M.: Aggregating template or rule antecedents in realtime expert systems with fuzzy set logic. In: Proceedings of the 22nd Annual IEEE Asilomar Conference on Signals, Systems, Computers, Pacific Grove, CA, pp. 681–689 (1988)Google Scholar
  17. 17.
    Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A sequential selection process in group decision making with a linguistic assessment approach. Information Sciences 85, 223–239 (1995)MATHCrossRefGoogle Scholar
  18. 18.
    Do, H., Rahm, E.: Comparison of schema matching evaluations. In: Proceedings of the second international workshop on Web Databases (German Informatics Society), pp. 221–237 (2002)Google Scholar
  19. 19.
    Torra, V.: The Weighted OWA operator. International Journal of Intelligent Systems 12, 153–166 (1997)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qiu Ji
    • 1
  • Weiru Liu
    • 1
  • Guilin Qi
    • 1
  • David A. Bell
    • 1
  1. 1.School of Electronics, Electrical Engineering and Computer ScienceQueen’s University BelfastBelfastUK

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