Multi-strategy Based Matching Technique for Ontology Integration

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)


In modern era, ontology integration is most prominent and challenging problem in various domains of data mining. It helps greatly on defining interoperability in information processing systems. Ontology integration institutes interoperability by deriving semantic similarity and correspondences between the entities of ontologies. Ontology matching plays an integral role in whole integration process by identifying the similarity measure between source and target ontologies using various matching techniques, e.g. Element-level techniques and Structure level techniques. The scope of this paper is to discuss existing ontology matching techniques and propose a multi-strategy ontology matching approach. In Proposed algorithm is a multi-strategy matching approach, where multiple similarity measure are combined and finally an ontology tree based on the binary tree is created. Ontology mapping is used for creating ontology binary tree. The hybrid approach of Ontology integration performs significantly better than its predecessor with single similarity measures.


Ontology String-based technique Structure-based technique Levenshtein distance Matching Similarity Ontology integration 


  1. 1.
    Berners-Lee, T., Handler, J., Lassila, O.: The Semantic Web. Scientific American (2001)Google Scholar
  2. 2.
    Huiping, J.: Information retrieval and the Semantic Web. In: IEEE International Conference on Educational and Information Technology (ICEIT), vol. 3, pp. 461–463. Chongqing, China (2010)Google Scholar
  3. 3.
    Perez-Lopez, A., Blace, R. Fisher, M., Hebeler, J.: Semantic Web Programming. Wiley Publishing, Inc., New York (2009)Google Scholar
  4. 4.
    Huang, L., Hu, G., Yang, X.: Review of ontology mapping. In: 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 537–540. Yichang (2012)Google Scholar
  5. 5.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data. Eng. 25(1), 158–176 (2013)Google Scholar
  6. 6.
    Ye, B., Chai, H., He, W., Wang, X., Song, G.: Semantic similarity calculation method in ontology mapping. In: 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS), vol. 3, pp. 1259–1262. Hangzhou (2012)Google Scholar
  7. 7.
    Xia, H., Zheng, X., Hu, X., Tian, Y.: Multi-strategy ontology mapping based on stacking method. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery (2009)Google Scholar
  8. 8.
    Fuqiang, L.,Yongfu, X.: The method of multi-strategy ontology mapping. In: ICCIS (2011)Google Scholar
  9. 9.
    Euzenat, E., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2013)Google Scholar
  10. 10.
    Rodrfguez, A., Egenhofer, M.: Determining semantic similarity among entity classes from diferent ontologies. IEEE Trans. Knowl. Data. Eng. 15(2), 442–456 (2003)Google Scholar
  11. 11.
    Li, C.M., Bo, S.J.: The research of ontology mapping method based on computing similarity. Sci. Technol. Inf. 1, 552–554 (2010)Google Scholar
  12. 12.
    Zhe, Y.: Semantic similarity match of ontology concept based on heuristic rules. Comput. Appl. 12 (2007)Google Scholar
  13. 13.
    Doan, A.H., Jayant, M., Pedro, D. et al.: Learning to map between ontologies on the semantic web. In: Eleventh International World Wide Web Conference, Honolulu Hawaii, USA (2005)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Computer Engineering DepartmentNIT KurukshetraKurukshetraIndia

Personalised recommendations