Information retrieval and machine learning for probabilistic schema matching

Nottelmann H., Straccia U.

Data exchange  Schema matching  Management Science and Operations Research  H.3 Information Storage and Retrieval  Computer Science Applications  Probability theory  Library and Information Sciences  information retrieval  Media Technology  Information Systems  Information retrieval  H.3.3 Information search and retrieval 

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.

Source: Information processing & management 43 (2007): 552–576. doi:10.1016/j.ipm.2006.10.014

Publisher: Pergamon,, New York , Regno Unito

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