Subject Knowledge, Source of Terms, and Term Selection in Query Expansion: An Analytical Study

  • Pertti Vakkari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2291)


The role of subject and search knowledge in query expansion (QE) is an unmapped terrain in research on information retrieval. It is likely that both have an impact on the process and outcome of QE. In this paper our aim is an analytical study of the connections between subject and search knowledge and term selection in QE based both on thesaurus and relevance feedback. We will also argue analytically how thesaurus, term suggestion in interactive QE and term extraction in automatic QE support users with differing levels of subject knowledge in their pursuit of search concepts and terms. It is suggested that in QE the initial query concepts representing the information need should not be treated as separate entities, but as conceptually interrelated. These interrelations contribute to the meaning of the conceptual construct, which the query represents, and this should be reflected in the terms identified for QE.


Term Selection Relevance Feedback Query Expansion Subject Knowledge Query Plan 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anick, P. & Tiperneni, S. The paraphrase search assistant: terminological feedback for iterative information seeking. In Proceedings of the SIGIR’99. ACM, New York (1999) 153–159Google Scholar
  2. 2.
    Bates, M. Subject access to online catologs: A design model. Journal of the American Society for Information Science 37 (6) (1986) 357–376CrossRefGoogle Scholar
  3. 3.
    Bates, M. Indexing and access for digital libraries and the internet: Human, database, and domain factors. Journal of the American Society for Information Science. 49 (13) (1998) 1185–1205CrossRefGoogle Scholar
  4. 4.
    Belkin, N. & Cool, C. & Head, J. & Jeng, J. Kelly, D. & al., Relevance Feedback versus Local Context Analysis as term suggestion devices: Rutgers’ TREC-8 interactive track experience. In: Proceedings of TREC-8. Available from: (20001)Google Scholar
  5. 5.
    Blair, D. & Maron, M. An evaluation of retrieval effectiveness for a full-text document retrieval system. Communications of the ACM. 28 (3) (1985) 289–299CrossRefGoogle Scholar
  6. 6.
    Borgman, C. Why are online catologs still hard to use? Journal of the American Society for Information Science. 47 (1996) 493–503.CrossRefGoogle Scholar
  7. 7.
    Brajnik, G., Mizzarro, S. & Tasso, C. Evaluating user interfaces to information retrieval systems. A case study. In Proceedings of the SIGIR’96. ACM, New York (1996) 128–136Google Scholar
  8. 8.
    Croft, B. & Das, R. Experiment with query acquisition and use in document retrieval systems. In: Proceedings of the SIGIR’90. Springer, Berlin (1990) 368–376Google Scholar
  9. 9.
    Eftimiadis, E. Query expansion. In: M.E. Williams (ed), Annual review of information science and technology, vol 31. N.J.: Information Today, Medford (1996) 121–187Google Scholar
  10. 10.
    Eftimiadis, E. Interactive query expansion: A user-based evaluation in relevance feedback environment. Journal of the American Society for Information Science. 51 (11) (2000) 989–1003CrossRefGoogle Scholar
  11. 11.
    Fenichel, C. The process of searching online bibliographic databases. Library Research 2 (1981) 107–127Google Scholar
  12. 12.
    Fidel, R. Searchers’ selection of search keys: III searching styles. Journal of the American Society for Information Science, 42 (1991) 515–527CrossRefGoogle Scholar
  13. 13.
    Fowkes, H., & Beaulieu, M. Interactive searching behavior: Okapi experiment for TREC-8. In Proceedings of the BCS-IRSG: 22nd Annual Colloquium on Information Retrieval Research. Cambridge (2000)Google Scholar
  14. 14.
    Greenberg, J. Automatic query expansion via lexical-semantic relationships. Journal of the American Society for Information Science, 52(2) (2001a) 402–415Google Scholar
  15. 15.
    Greenberg, J. Optimal query expansion processing methods with semantically encoded structured thesauri terminology. Journal of the American Society for Information Science, 52(6) (2001b) 487–498CrossRefGoogle Scholar
  16. 16.
    Hahn, U., & Chater, N. Concepts and similarity. In K. Lamberts, & D. Shanks (Eds.), Knowledge, concepts and categories. Psychology Press, Hove (1997) 43–92Google Scholar
  17. 17.
    Hancock-Beaulieu, M. & Fieldhouse, M. & Do, T. An evaluation of interactive query expansion in an online library catalogue with graphical user interface. Journal of Documentation. 51(3) (1995) 225–243CrossRefGoogle Scholar
  18. 18.
    Harter, S. Online information retrieval. Academic Press, Orlando (1986)Google Scholar
  19. 19.
    Hawking, D. & Thistlewaite, P. & Bailey, B. ANU/ACSys TREC-5 experiments. In: E. Voorhees & D. Harman (eds), Information technology: The fifth text retrieval conference (TREC-5). MD, Gaithersburg (1997) 359–375Google Scholar
  20. 20.
    Heit, E. Knowledge and concept learning. In K. Lamberts, & D. Shanks (Eds.), Knowledge, Concepts and Categories. Psychology Press, Hove (1997) 7–41Google Scholar
  21. 21.
    Hsieh-Yee, I. Effects of search experience and subject knowledge on the search tactics of novice and experienced searchers. Journal of the American Society for Information Science. 44 (1993) 161–174CrossRefGoogle Scholar
  22. 22.
    Ingwersen, P. Information retrieval interaction. Taylor Graham, London (1992)Google Scholar
  23. 23.
    Isenberg, D. Thinking and managing: a verbal protocol analysis of managerial problem solving. Academy of Management Journal. 20 (1986) 775–788.CrossRefGoogle Scholar
  24. 24.
    Jones, S & Gatford, M. & Robertson, S. & Hancock-Beaulieu, M. & Secker, J. Interactive thesaurus navigation: Intelligence rules OK? Journal of the American Society for Information Science. 46 (1) (1995) 52–59CrossRefGoogle Scholar
  25. 25.
    Järvelin, K. & Kekäläinen, J., IR evaluayion methods for highly relevant documents. Proceedings of the SIGIR’00. ACM, New York (2000) 41–48Google Scholar
  26. 26.
    Kekäläinen, J. The effects of query complexity, expansion and structure on retrieval performance in probabilistic text retrieval. Doctoral Dissertation. Tampere University Press, Tampere (1999)Google Scholar
  27. 27.
    Kekäläinen, J. & Järvelin, K. The impact of query structure and query extension on retrieval performance. In: Proceedings of the SIGIR’98. ACM, New York (1998) 130–137Google Scholar
  28. 28.
    Koenemann, J. & Belkin, N. A case for interaction: A study of interactive information retrieval behavior and effectiveness. In: Proceedings of the Human Factors in Computing Systems Conference (CHI’96). ACM Press, New York (1996) 205–212Google Scholar
  29. 29.
    Kuhlthau, C. Seeking Meaning. Norwood, N.J. Ablex (1993)Google Scholar
  30. 30.
    Lancaster, W & Warner, A. Information retrieval today. VA: Information Resources Press, Arlington (1993)Google Scholar
  31. 31.
    Lormand, E. How to be a meaning holist. The Journal of Philosophy. 93 (1996)Google Scholar
  32. 32.
    Marchionini, G. Information seeking in electronic environments. Cambridge University Press (1995)Google Scholar
  33. 33.
    Mitra, M.. & Singhal, A. & Buckley, C. Improving automatic query expansion. In Proceedings of the SIGIR’98. ACM, New York (1998) 206–214Google Scholar
  34. 34.
    Partridge, D., & Hussain K. Knowledge based information-systems. McGraw-Hill, London (1995)Google Scholar
  35. 35.
    Patel, V. & Ramoni, M. Cognitive models of directional inference in expert medical reasoning. In P. Feltovich, & K. Ford, & R. Hoffman (Eds.), Expertise in context: human and machine. AAAI Press, Menlo Park (Calif.) (1997) 67–99Google Scholar
  36. 36.
    Sormunen, E. A method of measuring wide range performance of Boolean queries in fulltext databases. Acta Universitatis Tamperensis 748. Doctoral Dissertation. Tampere University Press, Tampere (2000)Google Scholar
  37. 37.
    Sormunen, E. & Kekäläinen, J. & Koivisto, J. Järvelin, K. Document text characteristics affect the ranking of the most relevant documents by expanded structured queries. Journal of Documentation. 57 (3) (2001) 358–376.CrossRefGoogle Scholar
  38. 38.
    Spink, A., Greisdorf, R., & Bateman, J. From highly relevant to non-relevant: examining different regions of relevance. Information Processing & Management. 34 (1998) 599–622CrossRefGoogle Scholar
  39. 39.
    Stinchcombe, A. Constructing Social Theories. University of Chicago Press, Chicago (1987)Google Scholar
  40. 40.
    Sutcliffe, A., & Ennis, M. Towards a cognitive theory of IR. Interacting with Computers. 10 (1998) 321–351CrossRefGoogle Scholar
  41. 41.
    Sutcliffe, A., Ennis, M., & Watkinson, S. Empirical studies of end-user information searching. Journal of the American Society for Information Science. 51 (2000) 1211–1231CrossRefGoogle Scholar
  42. 42.
    Swanson, D. Historical note: Information retrieval and the future of an illusion. Journal of the American Society for Information Science. 39 (4) (1988) 92–98CrossRefGoogle Scholar
  43. 43.
    Turtle, H. & Croft, W. Evaluation of inference network-based retrieval model. ACM transactions on information systems. 9 (3) (1991) 187–222CrossRefGoogle Scholar
  44. 44.
    Vakkari, P. Cognition and changes of search terms and tactics during task performance. Proceedings of the RIAO’2000 Conference Paris: C.I.D. (2000) 894–907. Also:
  45. 45.
    Vakkari, P. A Theory of the Task-based Information Retrieval. Journal of Documentation. 57 (1) (2001) 44–60CrossRefGoogle Scholar
  46. 46.
    Vakkari, P., & Hakala, N. Changes in relevance criteria and problem stages in task performance. Journal of Documentation. 56 (2000) 540–562CrossRefGoogle Scholar
  47. 47.
    Vakkari, P. & Pennanen, M. & Serola, S. Changes of search terms and tactics while writing a research proposal: A longitudinal case study. Submitted for publication (2002)Google Scholar
  48. 48.
    Wang, P. User’s information needs at different stages of a research project: a cognitive view. In P. Vakkari, R. Savolainen, & B., Dervin (Eds.), Information Seeking in Context. Taylor, London & Los Angeles (1997) 307–318Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Pertti Vakkari
    • 1
  1. 1.Department of Information StudiesUniversity of TampereFinland

Personalised recommendations