Hyponymy Extraction and Web Search Behavior Analysis Based on Query Reformulation

  • Rui P. Costa
  • Nuno Seco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5290)


A web search engine log is a very rich source of semantic knowledge. In this paper we focus on the extraction of hyponymy relations from individual user sessions by examining, search behavior. The results obtained allow us to identify specific reformulation models as ones that more frequently represent hyponymy relations. The extracted relations reflect the knowledge that the user is employing while searching the web. Simultaneously, this study leads to a better understanding of web user search behavior.


Semantic Web Usage Mining Query Reformulation Hyponymy Extraction Web User Search Behavior 


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  1. 1.
    Jansen, B.J.: Search log analysis: What it is, what’s been done, how to do it. Library and Information Science Research 28, 407–432 (2006)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Rieh, S.Y., Xie, H.I.: Analysis of multiple query reformulations on the web: The interactive information retrieval context. Information Processing & Management 42, 751–768 (2006)CrossRefGoogle Scholar
  3. 3.
    Wang, P., Berry, M.W., Yang, Y.: Mining longitudinal web queries: Trends and patterns. J. Am. Soc. Inf. Sci. Technol. 54, 743–758 (2003)CrossRefGoogle Scholar
  4. 4.
    Chuang, S.L., Chien, L.F.: Enriching web taxonomies through subject categorization of query terms from search engine logs. Decision Support System 30 (2003)Google Scholar
  5. 5.
    Noriaki, K., Takeya, M., Miyoshi, H.: Semantic log analysis based on a user query behavior model. In: ICDM 2003: Proceedings of the Third IEEE International Conference on Data Mining, Washington, DC, USA, p. 107. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  6. 6.
    Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th International Conference on Computational Linguistics, Nantes, S2K-92-09 (1992)Google Scholar
  7. 7.
    de Freitas, M.C.: Elaboração automática de ontologias de domínio: discussão e resultados. PhD thesis, Universidade Católica do Rio de Janeiro (2007)Google Scholar
  8. 8.
    Buitelaar, P., Cimiano, P.: Ontology Learning and Population: Bridging the Gap between Text and Knowledge. Frontiers in Artificial Intelligence and Applications, vol. 167. IOS Press, Amsterdam (March 2008)zbMATHGoogle Scholar
  9. 9.
    Aires, R., Aluisio, S.: Como incrementar a qualidade dos resultados das maquinas de busca: da analise de logs a interaccao em portugues. Ciencia de Informacao 3, 5–16 (2003)Google Scholar
  10. 10.
    Seco, N., Cardoso, N.: Detecting user sessions in the tumba! query log (unpublished) (March 2006)Google Scholar
  11. 11.
    He, D., Göker, A.: Detecting session boundaries from web user logs. In: 22nd Annual Colloquium on Information Retrieval Research (2000)Google Scholar
  12. 12.
    Bruza, P., Dennis, S.: Query reformulation on the internet: Empirical data and the hyperindex search engine. In: RIA 1997 Conference Computer-Assisted Information Searching on Internet, pp. 488–499 (1997)Google Scholar
  13. 13.
    Saracevic, T.: The stratified model of information retrieval interaction: Extension and applications. In: 60th annual meeting of the American Society for Information Science, vol. 34, pp. 313–327 (1997)Google Scholar
  14. 14.
    Hancock-Beaulieu, M., Robertson, S., Nielsen, C.: Evaluation of online catalogues: An assessment of methods (bl research paper 78). The British Library Research and Development Department, London (1990)Google Scholar
  15. 15.
    Phippen, A., Sheppard, L., Furnell, S.: A practical evaluation of web analytics. Internet Research: Electronic Networking Applications and Policy 14, 284–293 (2004)CrossRefGoogle Scholar
  16. 16.
    Cimiano, P., Staab, S.: Learning by googling. SIGKDD Explor. Newsl. 6(2), 24–33 (2004)CrossRefGoogle Scholar
  17. 17.
    Collins, A.M., Quillian, M.R.: Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior 8, 240–247 (1969)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rui P. Costa
    • 1
  • Nuno Seco
    • 1
  1. 1.Cognitive and Media Systems GroupCISUC University of CoimbraPortugal

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