A probabilistic approach to schema matching

This paper introduces the first formal framework for learning mappings between heterogeneous schemas, which is based on probabilistic logics. This task, also called ``schema matching'', is a crucial step in integrating heterogeneous collections. As schemas may have different granularities, and as schema attributes do not always match precisely, a general-purpose schema mapping approach requires support for uncertain mappings, and mappings have to be learned automatically. The framework combines different classifiers for finding suitable mapping candidates (together with their weights), and selects that set of mapping rules which is the most likely one. Finally, the framework with different variants has been evaluated on two different data sets.