Collaborating Authors

Santos, Emanuel

Ontology alignment repair through modularization and confidence-based heuristics Artificial Intelligence

Ontology Matching aims to find a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, most of the alignments produced for large ontologies are logically incoherent. It was only recently that the use of repair techniques to improve the quality of ontology alignments has been explored. In this paper we present a novel technique for detecting incoherent concepts based on ontology modularization, and a new repair algorithm that minimizes the incoherence of the resulting alignment and the number of matches removed from the input alignment. An implementation was done as part of a lightweight version of AgreementMaker system, a successful ontology matching platform, and evaluated using a set of four benchmark biomedical ontology matching tasks. Our results show that our implementation is efficient and produces better alignments with respect to their coherence and f-measure than the state of the art repairing tools. They also show that our implementation is a better alternative for producing coherent silver standard alignments.

Testing the AgreementMaker System in the Anatomy Task of OAEI 2012 Artificial Intelligence

The AgreementMaker system was the leading system in the anatomy task of the Ontology Alignment Evaluation Initiative (OAEI) competition in 2011. While AgreementMaker did not compete in OAEI 2012, here we report on its performance in the 2012 anatomy task, using the same configurations of AgreementMaker submitted to OAEI 2011. Additionally, we also test AgreementMaker using an updated version of the UBERON ontology as a mediating ontology, and otherwise identical configurations. AgreementMaker achieved an F-measure of 91.8% with the 2011 configurations, and an F-measure of 92.2% with the updated UBERON ontology. Thus, AgreementMaker would have been the second best system had it competed in the anatomy task of OAEI 2012, and only 0.1% below the F-measure of the best system.