A probabilistic, logic-based framework for automated ontology matching

We introduces oPLMap, a formal framework for automatically learning mapping rules between heterogeneous ontologies, a crucial step in integrating ontologies and their instances in the Semantic Web. This approach is based on Horn predicate logics and probability theory, which allows for dealing with uncertain mappings (for cases where there is no exact correspondence between classes), and can be easily extended towards complex ontology models. Different components are combined for finding suitable mapping candidates (together with their weights), and the set of rules with maximum matching probability is selected. Our system oPLMap with different variants has been evaluated on a large test set.