Enhancing Information Retrieval Using Problem Specific Knowledge

  • Nobuyuki Morioka
  • Ashesh Mahidadia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4303)


In the recent past, information retrieval techniques have improved significantly and it is now possible to access massive text corpora using some of the most popular search engine tools like Google, Yahoo, PubMed (popular search engine for medical literature), etc. Considering that such search engine tools are normally trying to retrieve information from massive text corpora, a number of search results they need to display might be in hundreds or even in tens of thousands. Normally it is not possible or practical to browse through a very large collection of search results, and often a typical user needs further assistance in focusing on the search results that might best meet his/her requirements.

In this paper we present a new technique that allow a user to interactively express problem (task) specific knowledge (which is otherwise not possible using search engine tools like Google, Yahoo, PubMed, etc) and later use this knowledge to help a user to interactively and quickly focus on search results they might be interested in. The system presented in this paper integrates some of the techniques from the field of Natural Language Processing and Visualisation.


Intelligent Systems Information Retrieval Knowledge based Information Retrieval 


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  1. 1.
    Belkin, N., et al.: Evaluating Interactive Information Retrieval Systems: Opportunities and Challeges. In: Conference on Human Factors in Computing Systems (CHI 2004), ACM Press, New York (2004)Google Scholar
  2. 2.
    Jackson, P., Moulinier, I.: Natural Languare Processing for Online Applications. John Benjamins Publishing Company, Amsterdam (2002)Google Scholar
  3. 3.
    Ruthven, I.: Re-examining the Potential Effectiveness of Interactive Query Expansion. In: SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, ACM Press, New York (2003)Google Scholar
  4. 4.
    Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Journal of the Americal Society for Information Science 41(4), 288–297 (1990)CrossRefGoogle Scholar
  5. 5.
    Shen, X., Tan, B., Zhai, C.: Context-Sensitive Information Retrieval Using Implicit Feedback. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval SIGIR 2005, ACM Press, Brazil (2005)Google Scholar
  6. 6.
    Stojanovic, N.: On the Role of a User’s Knowledge Gap in an Information Retrieval Process. In: Proceedings of the 3rd International Conference on Knowledge Capture (K-CAP 2005), Banff, Alberta, Canada, ACM Press, New York (2005)Google Scholar
  7. 7.
    Voorhees, E.H., Harman, D.: Overview of the sixth text retrieval conference (TREC-6). In: Information Processing and Management (2000)Google Scholar
  8. 8.
    KartOO visual meta search engine,
  9. 9.
    Vivisimo’s clustering,
  10. 10.
    Müller, H.M., Kenny, E.E., Sternberg, P.W.: Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature. PLoS Biol. 2(11), e309 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nobuyuki Morioka
    • 1
  • Ashesh Mahidadia
    • 1
  1. 1.School of Computer Science and EngineeringThe University of New South WalesSydneyAustralia

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