Schema Evolution publication categorizer

This paper introduces the first formal framework for learning mappings
between heterogeneous schemas which is based on logics and probability
theory. 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.