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Modeling User Knowledge from Queries: Introducing a Metric for Knowledge

  • Frans van der Sluis
  • Egon L. van den Broek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6335)

Abstract

The user’s knowledge plays a pivotal role in the usability and experience of any information system. Based on a semantic network and query logs, this paper introduces a metric for users’ knowledge on a topic. The finding that people often return to several sets of closely related, well-known, topics, leading to certain concentrated, highly activated areas in the semantic network, forms the core of this metric. Tests were performed determining the knowledgeableness of 32,866 users on in total 8 topics, using a data set of more than 6 million queries. The tests indicate the feasibility and robustness of such a user-centered indicator.

Keywords

Semantic Network Knowledge Model Semantic Distance Unique Query Uncommon Word 
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.

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References

  1. Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of semantic distance. Computational Linguistics 32(1), 13–47 (2006)CrossRefGoogle Scholar
  2. Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: The vocabulary problem in human-system communication. ACM Commun. 30(11), 964–971 (1987)CrossRefGoogle Scholar
  3. Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. Gervasi, V., Ambriola, V.: Quantitative assessment of textual complexity. In: Merlini Barbaresi, L. (ed.) Complexity in Language and Text, pp. 197–228. Plus Pisa University Press, Pisa, Italy (2003)Google Scholar
  5. Kintsch, W.: Text comprehension, memory, and learning. American Psychologist 49(4), 294–303 (1994)CrossRefGoogle Scholar
  6. LaBerge, D., Samuels, S.J.: Toward a theory of automatic information processing in reading. Cognitive Psychology 6(2), 293–323 (1974)CrossRefGoogle Scholar
  7. Miller, G.A.: Wordnet: a lexical database for english. ACM Commun. 38(11), 39–41 (1995)CrossRefGoogle Scholar
  8. Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: Proc. 1st Intl. Conf. on Scalable Information Systems. ACM Press, New York (2006)Google Scholar
  9. Reeve, J.: The interest-enjoyment distinction in intrinsic motivation. Motivation and Emotion 13(2), 83–103 (1989)CrossRefGoogle Scholar
  10. Schraw, G., Lehman, S.: Situational interest: A review of the literature and directions for future research. Educational Psychology Review 13(30), 23–52 (2001)CrossRefGoogle Scholar
  11. Van der Sluis, F., Van den Broek, E.L.: Applying Ockham’s razor to search results: Using complexity measures in information retrieval. In: Information Interaction in Context (IIiX) Symposium, ACM, New York (in Press)Google Scholar
  12. Van der Sluis, F., Van den Broek, E.L., Van Dijk, E.M.A.G.: Information Retrieval eXperience (IRX): Towards a human-centered personalized model of relevance. In: Third International Workshop on Web Information Retrieval Support Systems, Toronto, Canada, August 31 (2010)Google Scholar
  13. Wedig, S., Madani, O.: A large-scale analysis of query logs for assessing personalization opportunities. In: KDD 2006: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 742–747. ACM, New York (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Frans van der Sluis
    • 1
  • Egon L. van den Broek
    • 1
  1. 1.Department of Human Media InteractionUniversity of TwenteEnschedeThe Netherlands

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