Generating Expressive Correspondences: An Approach Based on User Knowledge Needs and A-Box Relation Discovery

Ontology matching aims at making different ontologies interoperable. While most approaches have addressed the generation of simple correspondences, more expressiveness is required to better address the different kinds of ontology heterogeneities. This paper presents an approach for generating complex correspondences that relies on the notion of competency questions for alignment (CQA). A CQA expresses the user knowledge needs in terms of alignment and aims at reducing the alignment scope. The approach takes as input a set of CQAs as SPARQL queries over the source ontology. The generation of correspondences is performed by matching the subgraph from the source CQA to the lexically similar surroundings of the instances from the target ontology. Evaluation of the approach has been carried out on both synthetically generated and real-word datasets.