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A Hybrid NSGA-II for Matching Biomedical Ontology

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Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 109))

Abstract

Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Approaches based on Multi-Objective Evolutionary Algorithm (MOEA), such as NSGA-II, are emerging as a new methodology to solve the ontology matching problem. In this paper, to further improve the quality of biomedical ontology alignments, a hybrid NSGA-II is proposed, which modifies the knee solutions in the Pareto front by using a local search method. Experiment utilizes two biomedical ontology matching tracks provided by Ontology Alignment Evaluation Initiative (OAEI 2017). The experimental results show that our approach outperforms the participants of OAEI 2017 and NSGA-II based ontology matching technique.

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Notes

  1. 1.

    http://oaei.ontologymatching.org/2017/anatomy/index.html.

  2. 2.

    http://www.cs.ox.ac.uk/isg/projects/SEALS/oaei/2017/.

  3. 3.

    http://oaei.ontologymatching.org/2017.

  4. 4.

    http://oaei.ontologymatching.org/2017/results/index.html.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (Nos. 61503082 and 61403121), Natural Science Foundation of Fujian Province (No. 2016J05145), Fundamental Research Funds for the Central Universities (No. 2015B20214), Scientific Research Startup Foundation of Fujian University of Technology (No. GY-Z15007), Scientific Research Development Foundation of Fujian University of Technology (No. GY-Z17162) and Fujian Province Outstanding Young Scientific Researcher Training Project (No. GY-Z160149).

Author information

Authors and Affiliations

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

    Xingsi Xue & Jie Chen

  2. Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue & Jie Chen

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

    Xingsi Xue

  4. Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue

  5. College of IOT Engineering, Hohai University, Changzhou, Jiangsu, China

    Junfeng Chen

  6. Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian, China

    Dongxu Chen

Authors
  1. Xingsi Xue
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  2. Jie Chen
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  3. Junfeng Chen
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  4. Dongxu Chen
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Corresponding author

Correspondence to Xingsi Xue .

Editor information

Editors and Affiliations

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

    Jeng-Shyang Pan

  2. Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan

    Akinori Ito

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Pei-Wei Tsai

  4. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Canberra, ACT, Australia

    Lakhmi C. Jain

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Xue, X., Chen, J., Chen, J., Chen, D. (2019). A Hybrid NSGA-II for Matching Biomedical Ontology. In: Pan, JS., Ito, A., Tsai, PW., Jain, L. (eds) Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2018. Smart Innovation, Systems and Technologies, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-030-03745-1_1

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