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The problem of merging databases arises in many government and commercialapplications. Schema matching, a common first step, identifies equivalentfields between databases. We introduce a schema matching framework that buildsnonparametric Bayesian models for each field and compares them by computing theprobability that a single model could have generated both fields. Ourexperiments show that our method is more accurate and faster than the existinginstance-based matching algorithms in part because of the use of nonparametricBayesian models.

Nonparametric Bayesian Modeling for Automated Database Schema Matching