Information for "A Probabilistic-Logical Framework for Ontology Matching"

We applied the

reasoner Pellet (Sirin et al. 2007) to create the ground MLN formulation and used TheBeast2 (Riedel 2008) to convert the MLN formulations to the corresponding ILP instances. Finally, we applied the mixed integer programming solver

SCIP3 to solve the ILP.

Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of apriori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.

The framework is not only useful for aligning concepts and properties but can also include instance matching. For this purpose, one would only need to add a hidden predicate modeling instance correspondences. The resulting matching approach would immediately benefit from probabilistic joint inference, taking into account the interdependencies between terminological and instance correspondences.