Cluster Computing

, Volume 20, Issue 4, pp 3379–3391 | Cite as

Multi-domain ontology mapping based on semantics

  • Shengli Song
  • Xiang Zhang
  • Guimin Qin


Ontology mapping indicates the semantic interconnection between the concepts of ontologies, while multi-domain ontology mapping is usually used to solve the semantic interconnection problem between domain ontologies. However, due to the differences in the definition approaches, there exists the heterogeneity among the domain ontologies to a certain extent. This paper proposes a probability-based and similarity-based ontology mapping algorithm, the purpose of which is to calculate the similarity between the concepts of the multi-domain ontology. Using the ESA algorithm based on Wikipedia and the principle that the similarity between the concepts with the same name equals 1, the paper proposes a new concept, ontology mapping association graph, to represent mapping results. The experiments show that the accuracy rate of the probability-based and similarity-based ontology mapping algorithm can reach 80% on both two Chinese test sets, namely, WordSimilarity-353 and Words-240. Compared with other algorithms, it does stand out on the aspect of accuracy.


Probability-based ontology mapping Similarity-based ontology mapping Ontology mapping association graph 


  1. 1.
    Cerón-Figueroa, S., López-Yáñez, I., Alhalabi, W., et al.: Instance-based ontology matching for e-learning material using an associative pattern classifier. Comput. Hum. Behav. 69, 218–225 (2017)CrossRefGoogle Scholar
  2. 2.
    Doan, A.H., Madhavan, J., Dhamankar, R., et al.: Learning to match ontologies on the semantic web. VLDB J. 12(4), 303–319 (2003)CrossRefGoogle Scholar
  3. 3.
    Noy, N.F.: Ontology Mapping, pp. 30–52. Springer, Berlin (2009)Google Scholar
  4. 4.
    Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. Knowl. Eng. Rev. 18(1), 1–31 (2003)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lin, J.X.Y.X., Zhang, H.L.Y.: Advanced web technologies and applications. In: Asia-Pacific Web Conference, pp. 72–85 (2004)Google Scholar
  6. 6.
    Qiu, L., Yu, J., Pu, Q., et al.: Knowledge entity learning and representation for ontology matching based on deep neural networks. Cluster Comput. 20, 969–977 (2017)CrossRefGoogle Scholar
  7. 7.
    Husein, I.G., Akbar, S., Sitohang, B., et al.: Review of ontology matching with background knowledge. In: 2016 International Conference on Data and Software Engineering (ICoDSE), pp. 1–6. IEEE (2016)Google Scholar
  8. 8.
    Messaouda, F., Hadjer, G., Refinement, C.E.: Reuse of ontologies semantic mapping. In: IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), pp. 1–4. IEEE (2015)Google Scholar
  9. 9.
    Do, H.: Schema matching and mapping-based data integration. PhD thesis, Department of Computer Science, Universitt Leipzig (2006)Google Scholar
  10. 10.
    Hernandez, M., Miller, R., Haas, L., Yan, L., Howard Ho, C.T., Tian, X.: Clio: A semi-automatic tool for schema mapping. In: SIGMOD Record (2001)Google Scholar
  11. 11.
    Ehrig, M., Staab, S.: QOM-quick ontology mapping. In: International Semantic Web Conference, vol. 3298, pp. 683–697. (2004)Google Scholar
  12. 12.
    Boddy, M.: Anytime problem solving using dynamic programming. In: Proceedings of the Ninth National Conference on Artificial Intelligence, Anaheim, California, pp. 738–743. Shaker Verlag (1991)Google Scholar
  13. 13.
    Mitra, P., Wiederhold, G.: Resolving terminological heterogeneity in ontologies. In: Proceedings of the ECAI’02 Workshop on Ontologies and Semantic Interoperability (2002)Google Scholar
  14. 14.
    Noy, N.F., Musen, M.A.: The PROMPT suite: interactive tools for ontology merging and mapping. Int. J. Hum Comput. Stud. 59(6), 983–1024 (2003)CrossRefGoogle Scholar
  15. 15.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to map between ontologies on the semantic web. In: The Eleventh International WWW Conference, Hawaii, US (2002)Google Scholar
  16. 16.
    Kishore, R., Ramesh, R.: Ontologies: A Handbook of Principles, Concepts and Applications in Information Systems. Springer Science & Business Media, New York (2007)zbMATHGoogle Scholar
  17. 17.
    Bakhtiar, A.: Filsafat Agama I, cet. I hlm, vol. 169. Logos Wacana Ilmu, Jakarta (1997)Google Scholar
  18. 18.
    Chandrasekaran, B., Josephson, J.R., Benjamins, V.R.: What are ontologies, and why do we need them? IEEE Intell. Syst. Appl. 14(1), 20–26 (1999)CrossRefGoogle Scholar
  19. 19.
    Sarno, R., Anistyasari, Y., Fitri, R.: Semantic Search: Pencarian Berdasarkan Konten. Penerbit Andi, Yogyakarta (2012)Google Scholar
  20. 20.
    Krishnan, K., Krishnan, R., Muthumari, A.: A semantic-based ontology mapping–information retrieval for mobile learning resources. Int. J. Comput. Appl. 39, 169–178 (2017)Google Scholar
  21. 21.
    Liu, X., Cheng, B., Liao, J., et al.: OMI-DL: an ontology matching framework. IEEE Trans. Serv. Comput. 9(4), 580–593 (2016)CrossRefGoogle Scholar
  22. 22.
    Liu, X., Cao, L., Dai, W.: Overview on ontology mapping and approach. In: IEEE International Conference on Broadband Network and Multimedia Technology, pp. 592–595. IEEE (2011)Google Scholar
  23. 23.
    Ehrig, M., Sure, Y.: Ontology mapping-an integrated approah. In: Proc. of the 1st European Semantic Web Symposium, Heraklion, Greece, pp. 76–91. Springer, Berlin (2004)Google Scholar
  24. 24.
    Bouquet, P., Euzenat, J., Franoni, E., et al.: Speifiation of a common framework for characterizing alignment. Knowledge Web Deliverable 2.2.1v2, University of Karlsruhe (2004)Google Scholar
  25. 25.
    Liu, J., Zhang, X., Sun, W.: Review of ontology mapping representation mechanism. In: International Conference on Broadcast Technology and Multimedia Communication (2010)Google Scholar
  26. 26.
    Natalya, F.N.: Ontology mapping. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 573–590. Springer, Berlin (2009)Google Scholar
  27. 27.
    Zhou, Qingyuan, Luo, Jianjian: Artificial neural network based grid computing of E-government scheduling for emergency management. Comput. Syst. Sci. Eng. 30(5), 327–335 (2015)Google Scholar
  28. 28.
    Zhou, Qingyuan, Luo, Juan: The service quality evaluation of ecologic economy systems using simulation computing. Comput. Syst. Sci. Eng. 31(6), 453–460 (2016)MathSciNetGoogle Scholar
  29. 29.
    Zhou, Q.: Multi-layer affective computing model based on emotional psychology. Electron. Commer. Res. (2017). doi: 10.1007/s10660-017-9265-8
  30. 30.
    Zhou, Q., Luo, J.: The study on evaluation method of urban network security in the big data era. Intell. Autom. Soft Comput. (2017). doi: 10.1080/10798587.2016.1267444

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Software Engineering InstituteXidian UniversityXi’anChina
  2. 2.School of Computer Science and TechnologyXidian UniversityXi’anChina

Personalised recommendations