CANARD: An Approach for Generating Expressive Correspondences based on Alignment Need and ABox-based Relation Discovery

Ontology matching aims at making ontologies interoperable. While the field has fully developed in the last years, most approaches are still limited to the generation of simple correspondences. More expressiveness is however required to better address the different kinds of ontology heterogeneities. This paper presents CANARD (Complex Alignment Need and A-box based Relation Discovery), an approach for generating expressive 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 space. 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 similar surroundings of the instances from the target ontology. Evaluation is carried out on both synthetic and real-word datasets. The impact of several approach parameters is discussed.