An Alignment-Oriented Segmenting Approach for Optimizing Large Scale Ontology Alignments

TLDR: This paper proposes a generic alignment-oriented segmenting approach for optimizing the large scale ontology alignments through a neighbor based bottom-up partition algorithm to partition and introduces a Memetic Algorithm based matching technology to simultaneously match multiple pairs of ontology segments.

Abstract: Addressing ontology heterogeneity problem requires identifying correspondences between the entities across different ontologies, which is commonly known as ontology matching. However, the correct and complete identification of semantic correspondences are difficult to achieve with the larger searching space, thus achieving good efficiency is the major challenge for large scale ontology matching technologies. In this paper, we propose a generic alignment-oriented segmenting approach for optimizing the large scale ontology alignments. In particular, our proposal works in three sequential steps: first, using ontology semantic accuracy measure to determine the source ontology from two ontologies to align, and partitioning the source ontology into a set of disjoint segments through a neighbor based bottom-up partition algorithm to partition; then, utilizing a relevant concept filtering approach to determine the target ontology segments according to each source ontology segments; finally, a Memetic Algorithm (MA) based matching technology is introduced to simultaneously match multiple pairs of ontology segments to obtain final alignments. Four datasets in OAEI 2014, i.e., bibliographic benchmarks, anatomy track, library track and large biomedic track, are used to test our approach. The comparison between our approach and the participants in OAEI 2014 shows that our approach is effective.