On the Feasibility of Using OWL 2 Reasoners in Ontology Alignment Repair Problems

The problem of (semi-)automatically computing mappings between independently developed ontologies is usually referred to as the ontology matching problem. A number of sophisticated ontology matching systems have been developed in the last years [7, 26]. Ontology matching systems, however, rely on lexical and structural heuristics and the integration of the input ontologies and the mappings may lead to many undesired logical consequences (e.g., unsatisfiable classes). The fix of unsatisfiable classes caused by ontology mappings is known as the mapping repair problem [13]. Mapping repair can be addressed using state-of-the-art approaches for debugging inconsistencies in OWL 2 ontologies, which rely on the extraction of justifications for the unsatisfiable classes (e.g., [24, 14, 29, 12]). However, in [10] it was pointed out that justification-based technologies do not scale when the number of unsatisfiabilities is large (a typical scenario in mapping repair problems). In this paper we provide an update on the results presented in [10] by evaluating the feasibility of using up-to-date OWL 2 reasoners in mapping repair problems. We have conducted an extensive evaluation using the datasets and ontology matching systems from the Ontology Alignment Evaluation Initiative (OAEI) [7]. Our results suggest that the classification of the integration of large ontologies via mappings still poses a challenge to OWL 2 reasoners. Furthermore, the repair of unintended entailments (e.g., unsatisfiable concepts) using OWL 2 reasoners critically compromises the performance of mapping repair systems.