A Framework for Information Retrieval Based on Fuzzy Relations and Multiple Ontologies

  • Maria Angelica A. Leite
  • Ivan L. M. Ricarte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5290)


The use of knowledge in the information retrieval process allows the return of documents semantically related to the initial user’s query. This knowledge can be encoded in a knowledge base to be used in information retrieval systems. The framework for information retrieval based on fuzzy relations and multiple ontologies is a proposal to retrieve information using a knowledge base composed of multiple related ontologies whose relationships are expressed as fuzzy relations. Using this knowledge organization a new method to expand the user query is proposed. The framework provides a way that each ontology can be represented independently as well as their relationships. The proposed framework performance is compared with another fuzzy-based approach for information retrieval. Also the query expansion method is tested with the Apache Lucene search engine. In both cases the proposed framework improves the obtained results.


Fuzzy information retrieval ontology knowledge representation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. ACM Press / Addison-Wesley, New York (1999)Google Scholar
  2. 2.
    Ogawa, Y., Morita, T., Kobayashi, K.: A fuzzy document retrieval system using the keyword connection matrix and a learning method. In: Fuzzy Sets and Systems, vol. 39, pp. 163–179. Elsevier B. V, Amsterdam (1991)Google Scholar
  3. 3.
    Widyantoro, D.H., Yen, J.: A fuzzy ontology-based abstract search engine and its user studies. In: 10th IEEE International Conference on Fuzzy Systems, pp. 1291–1294. IEEE Computer Society, Washington (2001)Google Scholar
  4. 4.
    Bhogal, J., Macfarlane, A., Smith, P.: A review of ontology based query expansion. In: Information Processing and Management, vol. 43, pp. 866–886. Elsevier B. V, Amsterdam (2007)Google Scholar
  5. 5.
    Abulaish, M., Dey, L.: A fuzzy ontology generation framework for handling uncertainties and nonuniformity in domain knowledge description. In: International Conference on Computing: Theory and Applications, pp. 287–293. IEEE Computer Society, Washington (2007)CrossRefGoogle Scholar
  6. 6.
    Lau, R.Y.K., Li, Y., Xu, Y.: Mining fuzzy domain ontology from textual databases. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 156–162. IEEE Computer Society, Washington (2007)Google Scholar
  7. 7.
    Parry, D.: A fuzzy ontology for medical document retrieval. In: Second Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation, pp. 121–126. Australian Computer Society Inc., Darlinghurst (2004)Google Scholar
  8. 8.
    Gomez-Pérez, A., Fernández-Lopez, M., Corcho, O.: Ontological Engineering. Springer, London (2003)Google Scholar
  9. 9.
    Chen, S.M., Horng, Y.J., Lee, C.H.: Fuzzy information retrieval based on multi-relationship fuzzy concept networks. In: Fuzzy Sets and Systems, vol. 140, pp. 183–205. Elsevier B. V, Amsterdam (2003)Google Scholar
  10. 10.
    Horng, Y.J., Chen, S.M., Lee, C.H.: Automatically constructing multi-relationship fuzzy concept networks for document retrieval. In: Applied Artificial Intelligence, vol. 17, pp. 303–328. Taylor & Francis, Philadelphia (2003)Google Scholar
  11. 11.
  12. 12.
    Bratsas, C., Koutkias, V., Kaimakamis, E., Bamidis, P., Maglaveras, N.: Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions. In: 29th IEEE Annual International Conference on Engineering in Medicine and Biology Society, pp. 3794–3797. IEEE Computer Society, Washington (2007)CrossRefGoogle Scholar
  13. 13.
    Pereira, R., Ricarte, I., Gomide, F.: Fuzzy relational ontological model in information search systems. In: Sanchez, E. (ed.) Fuzzy Logic and The Semantic Web, pp. 395–412. Elsevier B. V, Amsterdam (2006)Google Scholar
  14. 14.
    Pedrycz, W., Gomide, F.: An introduction to fuzzy sets: Analysis and Design. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  15. 15.
  16. 16.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Maria Angelica A. Leite
    • 1
    • 2
  • Ivan L. M. Ricarte
    • 2
  1. 1.Embrapa Agriculture InformaticsCampinasBrazil
  2. 2.School of Electrical and Computer EngineeringUniversity of CampinasCampinasBrazil

Personalised recommendations