HOM: An Approach to Calculating Semantic Similarity Utilizing Relations between Ontologies

  • Zhizhong Liu
  • Huaimin Wang
  • Bin Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4993)


In the Internet environment, ontology heterogeneity is inevitable due to many coexistent ontologies. Ontology alignment is a popular approach to resolve ontology heterogeneity. Ontology alignment establishes the relation between entities by computing their semantic similarities using local or/and non-local contexts of entities. Besides local and non-local context of entities, the relations between two ontologies are helpful for computing their semantic similarity in many situations. The aim of this article is to improve the performance of ontology alignment by using these relations in similarity computing. A hierarchical Ontology Model (HOM) which describes these relations formally is proposed followed by HOM-Matching, an algorithm based on HOM. It makes use of the relations between ontologies to compute semantic similarity. Two groups of experiments are conducted for algorithm validation and parameters optimization.


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  1. Gruber, T.: Ontolingua: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  2. Bouquet, J.E.P., Franconi, E., Serafini, L., Stamou, G., Tessaris, S.: The state of art of ontology alignment. Deliverable D2.2.3. Knowledge web (2004)Google Scholar
  3. Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. The Knowledge Engineering Review 18(1), 1–31 (2003)CrossRefGoogle Scholar
  4. Ehrig, M., Sure, Y.: Ontology mapping - an integrated approach. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 76–91. Springer, Heidelberg (2004)Google Scholar
  5. N.F., Musen Noy, M.A.: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignmenteditors. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), Austin, TX (2000)Google Scholar
  6. Fikes, R., Mcguinness, D.L., Rice, J., Wilder, S.: An environment for merging and testing large ontologieseditors. In: Proceeding of KR 2000, pp. 483–493 (2000)Google Scholar
  7. Dieng, R., Hug, S.: Comparison of personal ontologies represented through conceptual graphs. In: Proc. of 13th ECAI 1998, Brighton, UK, pp. 341–345 (1998)Google Scholar
  8. Staab, S., Mädche, A.: Measuring similarity between ontologies. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 251–263. Springer, Heidelberg (2002)Google Scholar
  9. Noy, N., Musen, M.: Anchor-PROMPT: Using non-local context for semantic matchingeditors. In: Proc. IJCAI 2001 workshop on ontology and information sharing, Seattle, pp. 63–70 (2001)Google Scholar
  10. Zhang, S.M., Bodenreider, O.: Aligning Representations of Anatomy using Lexical and Structural Methods. In: 2003 editors Proceedings of AMIA Annual Symposium, USA, pp. 753–757 (2003)Google Scholar
  11. Bernstein, A., Kaufmann, E., Bürki, C., Klein, M.: Object Similarity in Ontologies: A Foundation for Business Intelligence Systems and High-Performance Retrieval. In: Proc. of 25th Int. Conf. on Information Systems, pp. 741–756 (2004)Google Scholar
  12. Oldakowski, R., Bizar, C.: SemMF: A Framework for Calculating Semantic Similarity of Objects Represented as RDF Graphseditors. In: 4th Int. Semantic Web Conference (2005) Google Scholar
  13. Hefke, V.Z.M., Abecker, A., Wang, Q.: An Extendable Java Framework for Instance Similarity in Ontologies. In: Yannis Manolopoulos, J.F., Constantopoulos, P., Cordeiro, J. (eds.) Proceedings of the Eighth International Conference on Enterprise Information Systems: Databases and Information Systems Integration, Paphos, Cyprus, pp. 263–269 (2006)Google Scholar
  14. Krötzsch, P.H.M., Ehrig, M.: York Sure Category. Theory in Ontology Research: Concrete Gain from an Abstract Approach. AIFB, Universität Karlsruhe (2005)Google Scholar
  15. Kent, R.: A KIF formalization of the IFF category theory ontology. In: Proc. IJCAI 2001 Workshop on the IEEE Standard Upper Ontology, Seattle Washington, USA (2001), http://citeseer.ist.psu.edu/kent01kif.html
  16. Zimmermann, M.K.A., Euzenat, J., Hitzler, P.: Formalizing Ontology Alignment and its Operations with Category Theory. In: Fellbaum, B.B.a.C. (ed.) Proceedings of the Fourth International Conference on Formal Ontology in Information Systems (FOIS 2006). Frontiers in Artificial Intelligence and Applications, vol. 150, pp. 277–288. IOS Press, Amsterdam (2006)Google Scholar
  17. Valtchev, P., Euzenat, J.: Dissimilarity Measure for Collections of Objects and Values. In: Liu, X., Cohen, P.R., R. Berthold, M. (eds.) IDA 1997. LNCS, vol. 1280, pp. 259–272. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  18. JWNL: Java WordNet Library (2004), http://sourceforge.net/projects/jwordnet

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zhizhong Liu
    • 1
  • Huaimin Wang
    • 1
  • Bin Zhou
    • 1
  1. 1.College of computer scienceNational University of defense TechnologyChangshaChina

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