Ontology alignment repair through modularization and confidence-based heuristics

TL;DR

This paper introduces a novel modularization and confidence-based heuristic approach for repairing incoherent ontology alignments, significantly improving coherence and F-measure in biomedical ontology matching tasks.

Contribution

It presents a new technique for detecting incoherent concepts via modularization and a repair algorithm that minimizes incoherence and match removal, integrated into a lightweight AgreementMaker system.

Findings

Improved coherence and F-measure over state-of-the-art tools.

Efficient implementation suitable for large ontologies.

Better performance in biomedical ontology matching benchmarks.

Abstract

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.