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Advances in Intelligent Information Hiding and Multimedia Signal Processing pp 37–45Cite as

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

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 157))

Abstract

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.

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Notes

  1. 1.

    Ontology Alignment Evaluation Initiative (OAEI), http://oaei.ontologymatching.org/2016, accessed at 2019–02–22.

References

  1. Xue, X., Wang, Y.: Using memetic algorithm for instance coreference resolution. IEEE Trans. Knowl. Data Eng. 28(2), 580–591 (2016)

    Article  Google Scholar 

  2. Xue, X., Pan, J.S.: A compact co-evolutionary algorithm for sensor ontology meta-matching. Knowl. Inf. Syst. 56(2), 335–353 (2018)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Richard Benjamins, V. (ed.): Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web. Springer Verlag, Berlin (2003)

    Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

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

    Jiawei Lu, Xingsi Xue & Guoxiang Lin

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

    Jiawei Lu & Xingsi Xue

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

    Xingsi Xue

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

    Xingsi Xue

  5. Concord University College, Fujian Normal University, Fuzhou, China

    Yikun Huang

Authors
  1. Jiawei Lu
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  2. Xingsi Xue
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  3. Guoxiang Lin
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  4. Yikun Huang
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Corresponding author

Correspondence to Xingsi Xue .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao Shi, Shandong, China

    Jeng-Shyang Pan

  2. Northeast Electric Power University, Chuanying Qu, Jilin, China

    Jianpo Li

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

    Pei-Wei Tsai

  4. Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Cite this paper

Lu, J., Xue, X., Lin, G., Huang, Y. (2020). A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_4

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