Bayesian Networks for Matcher Composition in Automatic Schema Matching
Daniel Nikolaev Nikovski, Alan Esenther, Xiang Ye, Mitsuteru Shiba, Shigenobu Takayama · 2012
We propose a method for accurate combining of evidence supplied by multiple individual match-ers regarding whether two data schema elements match (refer to the same object or concept), or not, in the field of automatic schema matching. The method uses a Bayesian network to model correctly the statistical correlations between the similarity values produced by individual match-ers that use the same or similar information, in order to avoid overconfidence in match probability estimates and improve the accuracy of matching. Experimental results under several testing pro-tocols suggest that the matching accuracy of the Bayesian composite matcher can significantly exceed that of the individual component matchers.