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CIKM
2005
Springer

Information retrieval and machine learning for probabilistic schema matching

10 years 2 months ago
Information retrieval and machine learning for probabilistic schema matching
Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distributed IR in federated digital libraries. This paper introduces a probabilistic framework, called sPLMap, for automatically learning schema mapping rules, based on given instances of both schemas. Different techniques, mostly from the IR and machine learning fields, are combined for finding suitable mapping candidates. Our approach gives a probabilistic interpretation of the prediction weights of the candidates, selects the rule set with highest matching probability, and outputs probabilistic rules which are capable to deal with the intrinsic uncertainty of the mapping process. Our approach with different variants has been evaluated on several test sets. Ó 2006 Elsevier Ltd. All rights reserved.
Henrik Nottelmann, Umberto Straccia
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CIKM
Authors Henrik Nottelmann, Umberto Straccia
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