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A segment-based approach for large-scale ontology matching

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Abstract

The most ground approach to solve the ontology heterogeneous problem is to determine the semantically identical entities between them, so-called ontology matching. However, the correct and complete identification of semantic correspondences is difficult to achieve with the scale of the ontologies that are huge; thus, achieving good efficiency is the major challenge for large- scale ontology matching tasks. On the basis of our former work, in this paper, we further propose a scalable segment-based ontology matching framework to improve the efficiency of matching large-scale ontologies. In particular, our proposal first divides the source ontology into several disjoint segments through an ontology partition algorithm; each obtained source segment is then used to divide the target ontology by a concept relevance measure; finally, these similar ontology segments are matched in a time and aggregated into the final ontology alignment through a hybrid Evolutionary Algorithm. In the experiment, testing cases with different scales are used to test the performance of our proposal, and the comparison with the participants in OAEI 2014 shows the effectiveness of our approach.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61503082 and 61402108), Natural Science Foundation of Fujian Province (No. 2016J05145) and China Scholarship Council.

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Authors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue & Jeng-Shyang Pan

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue & Jeng-Shyang Pan

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  1. Xingsi Xue
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  2. Jeng-Shyang Pan
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Correspondence to Xingsi Xue.

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Xue, X., Pan, JS. A segment-based approach for large-scale ontology matching. Knowl Inf Syst 52, 467–484 (2017). https://doi.org/10.1007/s10115-016-1018-9

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  • DOI: https://doi.org/10.1007/s10115-016-1018-9

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