CANARD: An Approach for Generating Expressive Correspondences based on Competency Questions for Alignment

Tracking #: 3521-4735

Elodie Thieblin
Guilherme Sousa
Ollivier Haemmerlé
Cassia Trojahn dos Santos

Responsible editor: 
Jérôme Euzenat

Submission type: 
Full Paper
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 rely 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-world datasets. The impact of several approach parameters is discussed. Experiments have showed that CANARD performs, overall, better on CQA coverage than precision and that using existing same:As links, between the instances of the source and target ontologies, gives better results than exact label matches of their labels. The use of CQA improved also both CQA coverage and precision with respect to using automatically generated queries. The reassessment of counter-example increased significantly the precision, to the detriment of runtime. Finally, experiments on large datasets showed that CANARD is one of the few systems that can perform on large knowledge bases, but depends on regularly populated knowledge bases and on the quality of instance links.
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