Fuzzy Semantic Similarity Between Ontological Concepts

  • Ling Song
  • Jun Ma
  • Hui Liu
  • Li Lian
  • Dongmei Zhang


The main focus of this paper concerns the measuring similarity in a content-based information retrieval and intelligent question-answering environment. While the measure of semantic similarity between concepts based on hierarchy in ontology is well studied, the measure of semantic similarity in an arbitrary ontology is still an open problem. In this paper we define a fuzzy semantic similarity measure based on information theory that exploits both the hierarchical and non-hierarchical structure in ontology. Our work can be generalized the following: firstly each concept is defined as a semantic extended fuzzy set along its semantic paths; secondly the semantic similarity between two concepts is computed with two semantic extended fuzzy sets instead of two concepts themselves. Our fuzzy measure considers some factors synthetically such as ontological semantic relation density, semantic relation depth and different semantic relations, which can affect the value of similarity. Compared with existed measures, this fuzzy similarity measure based on shared information content could reflect latent semantic relation of concepts better than ever.


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  1. [1]
    Ullas Nambiar and Subbarao Kambhampati, “Mining approximate functional dependencies and concept similarities to answer imprecise queries,” Seventh InternationalWorkshop on theWeb and Databases, Paris, France,2004, pp.73-78.Google Scholar
  2. [2]
    Ishwinder Kaur and Anthony J. Hornof, “A comparison of LSA, wordNet and PMI-IR for predicting user click behavior,” Conference on Human Factors in Computing Systems,Portland, Oregon, USA, 2005, pp.51 – 60.Google Scholar
  3. [3]
    Valerie Cross. “Fuzzy semantic distance measures between ontological concepts,” Fuzzy Information. 04, IEEE Annual Meeting of the Volume 2, Issue , 27-30 June 2004 Page(s): 635 - 640 Vol.2Google Scholar
  4. [4]
    Alexander Maedche1 and Steffen Staab. “Measuring similarity between ontologies,” Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management, Springer-Verlag, London, UK, 2002, pp. 251 – 263.Google Scholar
  5. [5]
    Vinay K. Chaudhri, Adam Farquhar Richard Fikes, Peter D. Karp and James P. Rice. OKBC: “A progammatic foundation for knowledge base interoperability,” Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, Madison, Wisconsin, United States, 1998, pp.600-607.Google Scholar
  6. [6]
    Andreas Hotho, Alexander Maedche and Steffen Staab, “Ontology-based text document clustering,” Scholar
  7. [7]
    Amos Tversky, ”Features of Similarity,” Psychological Review, 1977, 84(4): pp.327-352.CrossRefGoogle Scholar
  8. [8]
    Amos Tversky and Itamar Gati, “ Studies of similarity,” Scholar
  9. [9]
    Philip Resnik. “Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language,” Journal of Articial Intelligence Research, 1999, 11: pp95-130.Google Scholar
  10. [10]
    M. Andrea Rodríguez and Max J. Egenhofer, “Determining semantic similarity among entity classes from different ontologies,” IEEE Transactions on Knowledge and Data Engineering. 2003, 15(2): pp442 – 456.CrossRefGoogle Scholar
  11. [11]
    Peter Haase, Mark Hefke and Nenad Stojanovic, “Similarity for Ontologies - a comprehensive framework,” Scholar
  12. [12]
    Jay J. Jiang and David W. Conrath, “Semantic similarity based on corpus statistics and lexical taxonomy,” In Proceedings of International Conference Research on Computational Linguistics (ROCLING X), Taiwan, 1997.Google Scholar
  13. [13]
    Michael Sussna, “Word sense disambiguation for free-text indexing using a massive semantic network,” Proceedings of the Second International Conference on Information and Knowledge Management, Washington, D.C., United States, 1993, pp.67 - 74.Google Scholar
  14. [14]
    Wu, Z. and Palmer, M., “Verb semantics and lexical selection,” In Proceedings of the 32nd Annual Meeting of the Associations for Computational Linguistics, Las Cruces, New Mexico, 1994, pp. 133–138.Google Scholar
  15. [15]
    Dekang Lin, “An information-theoretic definition of similarity,” Proceedings of the Fifteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc, San Francisco, CA, USA,1998. pp.296 – 304.Google Scholar
  16. [16]
    Rolly Intan, “Rarity-based similarity relations in a generalized fuzzy information system,” Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, December 1-3, 2004, pp.462-467.Google Scholar
  17. [17]
    L. A. Zadeh, “Similarity relations and fuzzy orderings,” Information Science, 1970, 3(2): 177-200.CrossRefGoogle Scholar
  18. [18]
    Rolly Intan and Masao Muhidono, “A proposal of fuzzy thesaurus generated by fuzzy covering,” Fuzzy Information Processing Society-22nd International Conference of the North American, 2003, pp.167- 172.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Ling Song
    • 1
  • Jun Ma
    • 1
  • Hui Liu
    • 1
  • Li Lian
    • 1
  • Dongmei Zhang
    • 1
  1. 1.School of Computer Science & TechnologyShandong UniversityJinan, 250061

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