Learning Disjointness for Debugging Mappings between Lightweight Ontologies

Dealing with heterogeneous ontologies by means of semantic mappings has become an important area of research and a number of systems for discovering mappings between ontologies have been developed. Most of these systems rely on general heuristics for finding mappings, hence are bound to fail in many situations. Consequently, automatically generated mappings often contain logical inconsistencies that hinder a sensible use of these mappings. In previous work, we presented an approach for debugging mappings between expressive ontologies that eliminates inconsistencies by means of diagnostic reasoning. A shortcoming of this method was its need for expressive class definitions. More specifically, the applicability of this method critically relies on the existence of a high-quality disjointness axiomatization. This paper deals with the application of the debugging approach to mappings between lightweight ontologies that do not contain any or very few disjointness axioms, as it is the case for most of today’s practical ontologies. After discussing different approaches to deal with the absence of disjointness axioms we propose the application of supervised machine learning for detecting disjointness in a fully automatic manner. We present a detailed evaluation of our approach to learning disjointness and its impact on mapping debugging. The results show that debugging automatically created mappings with the help of learned disjointness axioms significantly improves the overall quality of these mappings.