Context-Based Query Expansion Method for Short Queries Using Latent Semantic Analyses

  • Btihal El GhaliEmail author
  • Abderrahim El Qadi
  • Mohamed Ouadou
  • Driss Aboutajdine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9466)


Short queries are the key difficulty in information retrieval (IR). A plenty of query expansion techniques has been proposed to solve this problem. In this paper, we propose three different models for query suggestion using the cosine similarity (CS), the Language Models (LM) or their fusion. The expansion terms are selected using the Latent Semantic Analyses method based on the result of the three query suggestion methods. The approaches proposed improve the precision of the user query by adding additional context to it. Experimental results show that expanding short queries by our approaches improves the effectiveness of the IR system by 48,1 % using the CS based model, 19,2 % using the LM model, and 13,5 % using the fusion model.


Query context Query suggestion LM LSA Query expansion 


  1. 1.
    Wen, J., Nie, J., Zhang, H.: Clustering user queries of a search engine. In: Proceedings of WWW10, Hong Kong, May 2001Google Scholar
  2. 2.
    Bouchard, H., Nie, J.Y.: Modèles de langue appliqués à la recherche d’information contextuelle. In: CORIA 2006, pp. 213–224, Lyon, France (2006)Google Scholar
  3. 3.
    Bai, J., Nie, J-Y. Bouchard, H., Cao, G.: Using query contexts in information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 15–22, New York, USA (2007)Google Scholar
  4. 4.
    Cui, H., Wen, J.R., Nie, J.Y., Ma, W.Y.: Probabilistic query expansion using query logs. In: WWW2002, Honolulu, Hawaii, USA, May 7–11 (2002)Google Scholar
  5. 5.
    Zahera, H.M., El Hady, G.F., Abd El-Wahed, W.F.: Query recommendation for improving search engine results. In: Proceedings of the World Congress on Engineering and Computer Science (WCECS 2010), vol. I, San Francisco USA, October (2010)Google Scholar
  6. 6.
    El Ghali, B., El Qadi, A., El Midaoui, O., Ouadou, M., Aboutajdine, D.: Probabilistic query expansion method based on a query recommendation algorithm. Int. J. Web Appl. (IJWA). 5(1), 1–12 (2013)Google Scholar
  7. 7.
    Cao, G., Nie, J., Bai, J.: Integrating word relationships into language models. In: Proceedings of SIGIR 2005, Salvador Brazil, August 2005Google Scholar
  8. 8.
    Zhai, C.: Statistical language models for information retrieval: a critical review. Found. Trends Inf. Retrieval 2(3), 137–215 (2008)CrossRefGoogle Scholar
  9. 9.
    Asfari, O., Doan, B-L., Bourda, Y., Sansonnet, J-P.: Context-based hybrid method for user query expansion. In: Proceedings of the Fourth International Conference on Advances in Semantic Processing, SEMAPRO 2010, pp. 69–74, Italy Florence (2010)Google Scholar
  10. 10.
    Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25, 259–284 (1998)CrossRefGoogle Scholar
  11. 11.
    Slimani, T., Ben Yaghlane, B., Mellouli, K.: Une extension de mesure de similarité entre les con-cepts d’une ontologie. In: Proceedings of SETIT 2007, 4th International Conference: Sciences of Electronic, Technologies of Information and Tele-Communications, Tunisia, March 2007Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Btihal El Ghali
    • 1
    Email author
  • Abderrahim El Qadi
    • 2
  • Mohamed Ouadou
    • 1
  • Driss Aboutajdine
    • 1
  1. 1.LRIT Associated Unit to the CNRST - URAC N°29 Faculty of ScienceMohammed V- UniversityRabatMorocco
  2. 2.TIMHigh School of Technology Moulay Ismaïl UniversityMeknesMorocco

Personalised recommendations