Learning complex mappings between ontologies

Abstract

In this paper, we introduce a new approach for constructing complex mappings between ontologies by transforming it to a rule learning process. Derived from the classical Inductive Logic Programming, our approach uses instance mappings as training data and employs tailoring heuristics to improve the learning efficiency. Empirical evaluation shows that our generated Horn-rule mappings are meaningful. © 2012 Springer-Verlag.