Ontology alignment repair through modularization and confidence-based heuristics
This work addresses the issue of incoherent alignments in ontology matching, particularly for large biomedical ontologies, offering an incremental improvement over existing repair techniques.
The paper tackles the problem of logical incoherence in ontology alignments for large ontologies by introducing a novel detection technique based on modularization and a repair algorithm that minimizes incoherence and match removal. The results show that their implementation is efficient and produces better alignments in terms of coherence and f-measure than state-of-the-art repairing tools on biomedical benchmark tasks.
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.