A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure

  • Jiawei Lu
  • Xingsi XueEmail author
  • Guoxiang Lin
  • Yikun Huang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)


Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal.


Ontology meta-matching Semantic similarity measure OAEI 



This work is supported by the Program for New Century Excellent Talents in Fujian Province University (No. GY-Z18155), the Program for Outstanding Young Scientific Researcher in Fujian Province University (No. GY-Z160149), the 2018 Program for Outstanding Young Scientific Researcher in Fujian, the Scientific Research Project on Education for Young and Middle-aged Teachers in Fujian Province (No. JZ170367), and the Scientific Research Foundation of Fujian University of Technology (No. GY-Z17162).


  1. 1.
    Xue, X., Wang, Y.: Using memetic algorithm for instance coreference resolution. IEEE Trans. Knowl. Data Eng. 28(2), 580–591 (2016)CrossRefGoogle Scholar
  2. 2.
    Xue, X., Pan, J.S.: A compact co-evolutionary algorithm for sensor ontology meta-matching. Knowl. Inf. Syst. 56(2), 335–353 (2018)CrossRefGoogle Scholar
  3. 3.
    Xue, X., Wang, Y.: Optimizing ontology alignments through a memetic algorithm using both MatchFmeasure and unanimous improvement ratio. Artif. Intell. 223, 65–81 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cai, Y., Zhang, Q., Lu, W., et al.: A hybrid approach for measuring semantic similarity based on IC-weighted path distance in WordNet. J. Intell. Inf. Syst. 51(1), 23–47 (2018)CrossRefGoogle Scholar
  5. 5.
    Xue, X., Wang, Y., Ren, A.: Optimizing ontology alignment through memetic algorithm based on partial reference alignment. Expert Syst. Appl. 41(7), 3213–3222 (2014)CrossRefGoogle Scholar
  6. 6.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)Google Scholar
  7. 7.
    Mascardi, V., Locoro, A., Rosso, P.: Automatic ontology matching via upper ontologies: a systematic evaluation. IEEE Trans. Knowl. Data Eng. 22(5), 609–623 (2010)CrossRefGoogle Scholar
  8. 8.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: WordNet: An Electronic Lexical Database, vol. 49, no 2, pp. 265–283 (1998)Google Scholar
  9. 9.
    Richard Benjamins, V. (ed.): Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web. Springer Verlag, Berlin (2003)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jiawei Lu
    • 1
    • 2
  • Xingsi Xue
    • 1
    • 2
    • 3
    • 4
    Email author
  • Guoxiang Lin
    • 1
  • Yikun Huang
    • 5
  1. 1.College of Information Science and EngineeringFujian University of TechnologyFuzhouChina
  2. 2.Intelligent Information Processing Research CenterFujian University of TechnologyFuzhouChina
  3. 3.Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina
  4. 4.Fujian Key Lab for Automotive Electronics and Electric DriveFujian University of TechnologyFuzhouChina
  5. 5.Concord University College, Fujian Normal UniversityFuzhouChina

Personalised recommendations