Generation of Query-Biased Concepts Using Content and Structure for Query Reformulation

  • Youjin Chang
  • Jun Wang
  • Mounia Lalmas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5039)

Abstract

This paper proposes an approach for query reformulation based on the generation of appropriate query-biased concepts. Query-biased concepts are generated from retrieved documents using their content and structure. In this paper, we focus on three aspects of the concept generation; the selection of query-biased concepts from retrieved documents, the effect of the structure, and the number of retrieved documents used for generating the concepts.

Keywords

query reformulation feature extraction concept generation structure relevance feedback 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, Y., Kim, M., Raghavan, V.V.: Construction of query concepts based on feature clustering of documents. Information Retrieval 9(3), 231–248 (2006)CrossRefGoogle Scholar
  2. 2.
    Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures and Algorithms. Prentice Hall, Englewood Cliffs (1992)Google Scholar
  3. 3.
    Malik, S., Lalmas, M., Fuhr, N.: Overview of INEX 2005. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds.) INEX 2005. LNCS, vol. 3977, pp. 1–15. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Nakata, K., Voss, A., Juhnke, M., Kreifelts, T.: Collaborative concept extraction from documents. In: 2nd International Conference on Practical Aspects of Knowledge management, pp. 29–30. Basel (1998)Google Scholar
  5. 5.
    Qiu, Y., Frei, H.P.: Concept based query expansion. In: 16th annual international ACM SIGIR conference on Research and Development in Information Retrieval, pp. 160–170. ACM press, Pittsburgh (1993)CrossRefGoogle Scholar
  6. 6.
    Rocchio, J.J.: Relevance Feedback in Information retrieval. In: Salton, G. (ed.) The SMART retrieval system – experiments in automatic document processing, pp. 313–323 (1971)Google Scholar
  7. 7.
    Rölleke, T., Lübeck, R., Kazai, G.: The HySpirit Retrieval Platform. In: ACM SIGIR Demonstration, New Orleans (2001)Google Scholar
  8. 8.
    Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowledge Engineering Review 18(1), 95–145 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Youjin Chang
    • 1
  • Jun Wang
    • 1
  • Mounia Lalmas
    • 1
  1. 1.Queen Mary, University of LondonLondonUK

Personalised recommendations