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An Interactive Multi-Objective Ontology Matching Technique

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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))

Abstract

Due to the heterogeneity problem between ontologies caused by the subjectivity of ontology builders, implementing the communication between ontologies in the same domain is hindered. Currently, ontology matching has been regarded as an effective method to solve this problem. As the problem of ontology matching is nonlinear, it is not easy to deal with it. However, matching two ontologies under alignment is a non-trivial task. In this paper, the approximate evaluation functions are proposed to avoid the requirement of external reference alignment that should be provided by the experts beforehand. To improve the quality of the obtained alignment, Interactive Multi-Objective Ontology Matching technique (IMOOM) is proposed. In particular, our proposal is able to screen out three representative solutions for users. Finally, the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) is used to verify the method proposed in this paper, and the experiment results show the effectiveness of our approach.

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Notes

  1. 1.

    http://oaei.ontologymatching.org/.

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Author information

Authors and Affiliations

  1. Taiyuan University of Technology, Taiyuan, China

    Qing Lv, Chengcai Jiang & He Li

Authors
  1. Qing Lv
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  2. Chengcai Jiang
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  3. He Li
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Corresponding author

Correspondence to Chengcai Jiang .

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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Lv, Q., Jiang, C., Li, H. (2021). An Interactive Multi-Objective Ontology Matching Technique. 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_89

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