An ontology-based semantic similarity metric to empower semantic search

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Heterogeneity in documents is a challenge for information Retrieval. The keyword search focuses on matching the keywords with web repositories. It does not consider the synonyms or semantically similar words. The heterogeneity of the content makes retrieval inadequate. Semantic search helps to capture more appropriate results using domain ontology. Keyword search is extended with the help of similar concepts of ontology. Similarity between the ontological concepts is recognized to get appropriate search results. Once the semantic similarity among the concepts is known, more relevant documents can be retrieved. In this paper, we propose a metric based on traditional methods, combined with computational techniques to measure the similarity between concepts. The paper gives the concept of DOT (Domain Ontology Tree). It uses conventional definitions of the Tree (Data Structure) for ontology and proposes a method of partitioning to calculate the similarity. The method is based on IS-A hierarchical relationship. We have implemented a prototype system for the support of the proposed method, and also compared it with existing methods, the results are encouraging.


  • Keywords

    Heterogeneity; Ontology; Metric; Semantic Similarity; Domain Ontology Tree (Dot).

  • References

      [1] Djamel Guessoum, Moeiz Miraoui, Chakib Tadj "modification of Wu and Palmer Semantic Similarity Measure "UBICOMM 2016: The Tenth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, pp 42-46. 2016. ISBN: 978-1-61208-505-0.

      [2] Zhao Guozeng "Research on concept similarity calculation method based on the semantic grid" Journal of Chemical and Pharmaceutical Research, 2015, 7 (3): 476-481ISSN: 0975-7384CODEN (USA).

      [3] Wenjie Li, Qiuxiang Xia," A Method of Concept Similarity Computation Based on Semantic Distance "Advanced in Control Engineering and Information Science. SciVerseScienceDirect procedia Engineering 15 (2011) 3854 – 3859, 877-7058 © 2011 Published by Elsevier Ltd.

      [4] Jike Ge, Yuhui Qiu,"Concept Similarity Matching Based on Semantic Distance" Fourth International Conference on Semantics, Knowledge and Grid 0-7695-3401-5/08 2008 IEEE.

      [5] Z. Wu and M. Palmer. “Verb semantics and lexical selection”. In Proceedings of the 32nd Annual meeting of the Associations for Computational Linguistics, pp 133-138. 1994.

      [6] D. Lin. “An Information-Theoretic Definition of similarity”. In Proceedings of the fifteenth International Conference on MachineLearning (ICML'98).MorganKaufmann: MadisonWI, pp.296-304.1998.

      [7] P. Resnik (1995). “Using information content to evaluate semantic similarity in taxonomy”. In Proceedings of 14th International Joint Conference on Artificial Intelligence, Montreal, 1995.

      [8] N. Ho and F. Cédrick. “Lexical Similarity based on Quantity of Information Exchanged-Synonym Extraction”. In the Proceeding of Conf. RIVF’04, February 2-5, 2004. Hanoi, Vietnam.

      [9] J.H. Lee, M.H. Kim and Y.J. Lee. “Information Retrieval Based on Conceptual Distance in IS-A Hierarchy”. Journal of Documentation 49, pp 188-207, 1993.

      [10] Rada, H. Mili, E. Bichnell, and M. Blettner, “Development and application of a metric ton semantic net”. IEEE Transaction on Systems, Man, and Cybernetics. pp 17-30. 1989.

      [11] J. Zhong, H. Zhu, J. Li and Y. Yu, “Conceptual graph matching for semantic search,” The 2002 International Conference on Computational Science (ICCS2002), Amsterdam, pp. 92-106, 2002.

      [12] Y. Ganjisaffar, H. Abolhassani, M. Neshati and M. Jamali, “A Similarity Measure for OWL-S Annotated Web Services,” Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 621-624, 2006.

      [13] Troy Simpson, Thanh Dao, “WordNet-based semantic similarity measurment”2010.

      [14] R. C. Veltkamp and L.J. Latecki. “Properties and Performances of Shape Similarity Measures”. 2006.

      [15] M. Dean, G. Schreiber, S. Bechhofer, F. van Harmelen, J.Hendler, I. Horrocks, and L. A. Stein, “OWL web ontology language reference,” W3C Recommendation February 10, 2004.




Article ID: 14153
DOI: 10.14419/ijet.v7i4.14153

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.