Measuring the Relative Performance of Schema Matchers

Authors:
Shlomo Berkovsky;Yaniv Eytani;Avigdor Gal
Affiliations:
University of Haifa;University of Haifa;Technion - Israel Institute of Technology
Venue:
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2005

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Abstract

Schema matching is a complex process focusing on matching between concepts describing the data in heterogeneous data sources. There is a shift from manual schema matching, done by human experts, to automatic matching, using various heuristics (schema matchers). In this work, we consider the problem of linearly combining the results of a set of schema matchers. We propose the use of machine learning algorithms to learn the optimal weight assignments, given a set of schema matchers. We also suggest the use of genetic algorithms to improve the process efficiency.