Managing Uncertainty in Schema Matcher Ensembles

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Managing Uncertainty in Schema Matcher Ensembles
Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources. With many heuristics to choose from, several tools have enabled the use of schema matcher ensembles, combining principles by which different schema matchers judge the similarity between concepts. In this work, we investigate means of estimating the uncertainty involved in schema matching and harnessing it to improve an ensemble outcome. We propose a model for schema matching, based on simple probabilistic principles. We then propose the use of machine learning in determining the best mapping and discuss its pros and cons. Finally, we provide a thorough empirical analysis, using both real-world and synthetic data, to test the proposed technique. We conclude that the proposed heuristic performs well, given an accurate modeling of uncertainty in matcher decision making.
Anan Marie, Avigdor Gal
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where SUM
Authors Anan Marie, Avigdor Gal
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