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A Large Scale Multi-objective Ontology Matching Framework

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Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 81))

Abstract

Multi-Objective Evolutionary Algorithm (MOEA) is emerging as a state-of-the-art methodology to solve the ontology meta-matching problem. However, the huge search scale of large scale ontology matching problem stops MOEA based ontology matching technology from correctly and completely identifying the semantic correspondences. To this end, in this paper, a large scale multi-objective ontology matching framework is proposed, which works with three sequential steps: (1) partition the large scale ontologies into similar ontology segment pairs; (2) utilize MOEA to match the similar ontology segments in parallel; (3) select the representative ontology segment alignments, which are further aggregated to obtain the final ontology alignment. In addition, a novel multi-objective model is also constructed for ontology matching problem and the MOEA and entity similarity measure that could be used in this framework are also recommended. The experimental result shows the effectiveness of our proposal.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (No. 61503082), Natural Science Foundation of Fujian Province (No. 2016J05145), Scientific Research Startup Foundation of Fujian University of Technology (No. GY-Z15007), Fujian Province outstanding Young Scientific Researcher Training Project (No. GY-Z160149) and China Scholarship Council.

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Authors and Affiliations

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

    Xingsi Xue

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

    Xingsi Xue

  3. Department of Mathematics, Baoji University of Arts and Sciences, Baoji, Shaanxi, China

    Aihong Ren

Authors
  1. Xingsi Xue
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  2. Aihong Ren
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Corresponding author

Correspondence to Xingsi Xue .

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Editors and Affiliations

  1. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology , Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Swinburne University of Technology, Hawthorn, Victoria, Australia

    Pei-Wei Tsai

  3. Universiti Teknologi Petronas, Teronoh, Malaysia

    Junzo Watada

  4. University of Canberra, Bruce, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Xue, X., Ren, A. (2018). A Large Scale Multi-objective Ontology Matching Framework. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-319-63856-0_31

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  • DOI: https://doi.org/10.1007/978-3-319-63856-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63855-3

  • Online ISBN: 978-3-319-63856-0

  • eBook Packages: EngineeringEngineering (R0)

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