LCS: A Linguistic Combination System for Ontology Matching
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.
KeywordsAggregation Operator Semantic Interpretation Ordered Weighted Average Ordered Weighted Average Operator Ontology Match
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- 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.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
- 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.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.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
- 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.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.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
- 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