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

Xingsi Xue, Shu-Chuan Chu

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    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 alignmentoriented 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.

    Original languageEnglish
    Pages (from-to)1373-1382
    Number of pages10
    JournalJournal of Internet Technology
    Volume17
    Issue number7
    DOIs
    Publication statusPublished - 2016

    Keywords

    • Large scale ontology matching
    • Memetic algorithm
    • Ontology partition algorithm

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