Advertisement

The Effective Relevance Link between a Document and a Query

  • Karam Abdulahhad
  • Jean-Pierre Chevallet
  • Catherine Berrut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7446)

Abstract

This paper proposes to understand the retrieval process of relevant documents against a query as a two-stage process: at first an identification of the reason why a document is relevant to a query that we called the Effective Relevance Link, and second the valuation of this link, known as the Relevance Status Value (RSV). We present a formal definition of this semantic link between d and q. In addition, we clarify how an existing IR model, like Vector Space model, could be used for realizing and integrating this formal notion to build new effective IR methods. Our proposal is validated against three corpuses and using three types of indexing terms. The experimental results showed that the effective link between d and q is very important and should be more taken into consideration when setting up an Information Retrieval (IR) Model or System. Finally, our work shows that taking into account this effective link in a more explicit and direct way into existing IR models does improve their retrieval performance.

Keywords

Information Retrieval Language Model Mean Average Precision Vector Space Model Indexing Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abdulahhad, K., Chevallet, J.-P., Berrut, C.: Solving concept mismatch through bayesian framework by extending umls meta-thesaurus. In: la huitième édition de la COnférence en Recherche d’Information et Applications (CORIA 2011), Avignon, France, March 16–18 (2011)Google Scholar
  2. 2.
    Amati, G., Van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20(4), 357–389 (2002)CrossRefGoogle Scholar
  3. 3.
    Aronson, A.R.: Metamap: Mapping text to the UMLS metathesaurus (2006)Google Scholar
  4. 4.
    Buckley, C., Salton, G., Allan, J., Singhal, A.: Automatic Query Expansion Using SMART: TREC 3. In: TREC (1994)Google Scholar
  5. 5.
    Chiaramella, Y., Chevallet, J.P.: About retrieval models and logic. Comput. J. 35, 233–242 (1992)zbMATHCrossRefGoogle Scholar
  6. 6.
    Chiaramella, Y., Mulhem, P., Fourel, F.: A model for multimedia information retrieval. Technical report (1996)Google Scholar
  7. 7.
    Clinchant, S., Gaussier, E.: Information-based models for ad hoc ir. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 234–241. ACM, New York (2010)Google Scholar
  8. 8.
    Crestani, F.: Exploiting the similarity of non-matching terms at retrievaltime. Inf. Retr. 2(1), 27–47 (2000)CrossRefGoogle Scholar
  9. 9.
    Dominich, S.: Mathematical Foundations of Information Retrieval, 1st edn. Mathematical Modelling: Theory and Applications. Springer (March 2001)Google Scholar
  10. 10.
    Fang, H., Tao, T., Zhai, C.: A formal study of information retrieval heuristics. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 49–56. ACM, New York (2004)Google Scholar
  11. 11.
    Losada, D.E., Barreiro, A.: A logical model for information retrieval based on propositional logic and belief revision. The Computer Journal 44, 410–424 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Nie, J.: An outline of a general model for information retrieval systems. In: Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1988, pp. 495–506. ACM, New York (1988)CrossRefGoogle Scholar
  13. 13.
    Ponte, J.M., Bruce Croft, W.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 275–281. ACM, New York (1998)CrossRefGoogle Scholar
  14. 14.
    Robertson, S.E.: The probability ranking principle in IR. In: Readings in Information Retrieval, pp. 281–286. Morgan Kaufmann Publishers Inc., San Francisco (1997)Google Scholar
  15. 15.
    Robertson, S.E., Walker, S.: Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1994, pp. 232–241. Springer-Verlag New York, Inc., New York (1994)Google Scholar
  16. 16.
    Rocchio, J.: Relevance Feedback in Information Retrieval, pp. 313–323 (1971)Google Scholar
  17. 17.
    Rose, D.E., Stevens, C.: V-twin: A lightweight engine for interactive use. In: TREC (1996)Google Scholar
  18. 18.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM (18), 613–620 (1975)Google Scholar
  19. 19.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)Google Scholar
  20. 20.
    Singhal, A., Buckley, C., Mitra, M.: Pivoted document length normalization. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1996, pp. 21–29. ACM, New York (1996)CrossRefGoogle Scholar
  21. 21.
    van Rijsbergen, C.J.: A non-classical logic for information retrieval. Comput. J. 29(6), 481–485 (1986)zbMATHCrossRefGoogle Scholar
  22. 22.
    Wilkinson, R., Zobel, J., Sacks-Davis, R.: Similarity measures for short queries. In: TREC (1995)Google Scholar
  23. 23.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2001, pp. 334–342. ACM, New York (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Karam Abdulahhad
    • 1
  • Jean-Pierre Chevallet
    • 2
  • Catherine Berrut
    • 1
  1. 1.LIG Laboratory, MRIM GroupUJF-Grenoble 1France
  2. 2.LIG Laboratory, MRIM GroupUPMF-Grenoble 2France

Personalised recommendations