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Matching Ontologies Through Evolutionary Algorithm with Context-Based Reasoning

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

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Abstract

Ontology matching can solve the heterogeneity problem between two ontologies, and EA represents a state-of-the-art technique for matching ontologies. However, there are two defects concerning the EA-based ontology matching technique: (1) a reference alignment between two ontologies to be matched is required in advance; (2) the confidence of entity similarity measure is low computational complexity of measuring the similarity value is high. To overcome these drawbacks, in this paper, an Evolutionary Algorithm with Context-based Reasoning method (EA-CR) is proposed, where: (1) an approximate metric without the reference alignment is utilized for evaluating the alignment’s quality; (2) a Context-based Reasoning method is presented to distinguish the heterogeneous entities. The experimental results show that the proposed approach is effective.

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Acknowledgments

This work is supported by the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (No. YQ20206), the Program for New Century Excellent Talents in Fujian Province University (No. GY-Z18155), the Scientific Research Foundation of Fujian University of Technology (No. GY-Z17162), the Science and Technology Planning Project in Fuzhou City (No. 2019-G-40) , the Foreign Cooperation Project in Fujian Province (No. 2019I0019), the National Natural Science Foundation of China (No. 61662018) and Guangxi Natural Science Foundation of China (No. 2018GXNSFAA050028).

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Authors and Affiliations

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

    Xingsi Xue

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

    Chaofan Yang

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

    Chaofan Yang

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

    Xingsi Xue

  5. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China

    Xingsi Xue

  6. School of Information and Communication, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China

    Cai Yue

Authors
  1. Chaofan Yang
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  2. Xingsi Xue
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  3. Cai Yue
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Corresponding author

Correspondence to Xingsi Xue .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

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

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

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Yang, C., Xue, X., Yue, C. (2021). Matching Ontologies Through Evolutionary Algorithm with Context-Based Reasoning. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_90

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