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CMC: Combining Multiple Schema-Matching Strategies Based on Credibility Prediction

  • KeWei Tu
  • Yong Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3453)

Abstract

Schema matching is a key operation in data engineering. Combining multiple matching strategies is a very promising technique for schema matching. To overcome the limitations of existing combination systems and to achieve better performances, in this paper the CMC system is proposed, which combines multiple matchers based on credibility prediction. We first predict the accuracy of each matcher on the current matching task, and accordingly calculate each matcher’s credibility. These credibilities are then used as weights in aggregating the matching results of different matchers into a combined one. Our experiments on real world schemas validate the merits of our system.

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References

  1. 1.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB Journal: Very Large Data Bases 10, 334–350 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Doan, A., Domingos, P., Halevy, A.Y.: Reconciling schemas of disparate data sources: a machine-learning approach. SIGMOD Record 30, 509–520 (2001)CrossRefGoogle Scholar
  3. 3.
    Do, H.H., Rahm, E.: COMA — A System for Flexible Combination of Schema Matching Approaches. In: VLDB 2002, pp. 610–621. Morgan Kaufmann Publishers, San Francisco (2002)Google Scholar
  4. 4.
    Berlin, J., Motro, A.: Database Schema Matching Using Machine Learning with Feature Selection. In: Pidduck, A.B., Mylopoulos, J., Woo, C.C., Ozsu, M.T. (eds.) CAiSE 2002. LNCS, vol. 2348, p. 452. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Perrone, M.P., Cooper, L.N.: When Networks Disagree: Ensemble Method for Neural Networks. In: Neural Networks for Speech and Image processing (1993)Google Scholar
  6. 6.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • KeWei Tu
    • 1
  • Yong Yu
    • 1
  1. 1.Department of Computer Science and EngineeringShanghai JiaoTong UniversityShanghaiP.R.China

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