A Hybrid Approach Using Ontology Similarity and Fuzzy Logic for Semantic Question Answering

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

One of the challenges in information retrieval is providing accurate answers to a user’s question often expressed as uncertainty words. Most answers are based on a Syntactic approach rather than a Semantic analysis of the query. In this paper our objective is to present a hybrid approach for a Semantic question answering retrieval system using Ontology Similarity and Fuzzy logic. We use a Fuzzy Co-clustering algorithm to retrieve collection of documents based on Ontology Similarity. Fuzzy scale uses Fuzzy type-1 for documents and Fuzzy type-2 for words to prioritize answers. The objective of this work is to provide retrieval systems with more accurate answers than non-fuzzy Semantic Ontology approach.

Keywords

Question and Answering Fuzzy Ontology Fuzzy type-1 Fuzzyc type-2 Semantic Web 

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References

  1. 1.
    Ohler, J.: The semantic web in education. Educause Quarterly 31(4), 7–9 (2008)Google Scholar
  2. 2.
    Agrawal, R.: Computational education: The next frontier for digital libraries (2013)Google Scholar
  3. 3.
    Mendel, J.M.: Type-2 fuzzy sets and systems: an overview. IEEE Computational Intelligence Magazine 2(1), 20–29 (2007)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Gallova, S.: Fuzzy ontology and information access on the web. IAENG International Journal of Computer Science 34(2) (2007)Google Scholar
  5. 5.
    Kwok, C., Etzioni, O., Weld, D.S.: Scaling question answering to the web. ACM Transactions on Information Systems 19(3), 242–262 (2001)CrossRefGoogle Scholar
  6. 6.
    Guo, Q., Zhang, M.: Question answering system based on ontology and semantic web. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 652–659. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Kaladevi, A.C., Kangaiammal, A., Padmavathy, S., Theetchenya, S.: Ontology extraction for e-learning: A fuzzy based approach. In: 2013 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6 (2013)Google Scholar
  8. 8.
    Mendel, J.: Fuzzy sets for words: why type-2 fuzzy sets should be used and how they can be used. presented as two-hour tutorial at IEEE FUZZ, Budapest, Hongrie (2004)Google Scholar
  9. 9.
    Benamara, F., Saint-Dizier, P.: Advanced relaxation for cooperative question answering. In: New Directions in Question Answering. MIT Press, Massachusetts (2004)Google Scholar
  10. 10.
    Bobilloa, F., Stracciab, U.: Aggregation operators for fuzzy ontologies. Applied Soft Computing 13(9), 3816–3830 (2013)CrossRefGoogle Scholar
  11. 11.
    Lord, P.: The semantic web takes wing: Programming ontologies with tawny-owl. arXiv preprint arXiv:1303.0213 (2013)Google Scholar
  12. 12.
    Rani, M., Kumar, S., Yadav, V.K.: Optimize space search using fcc_stf algorithm in fuzzy co-clustering through search engine. International Journal of Advanced Research in Computer Engineering & Technology 1, 123–127 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Indian Institute of Information TechnologyAllahabadIndia
  2. 2.School of Computing, Mathematics and Digital TechnologyManchester Metropolitan UniversityManchesterU.K

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