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Measuring the Relative Performance of Schema Matchers

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Measuring the Relative Performance of Schema Matchers
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.
Shlomo Berkovsky, Yaniv Eytani, Avigdor Gal
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where WEBI
Authors Shlomo Berkovsky, Yaniv Eytani, Avigdor Gal
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