GOOSE: A Goal-Oriented Search Engine with Commonsense

  • Hugo Liu
  • Henry Lieberman
  • Ted Selker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2347)


A novice search engine user may find searching the web for information difficult and frustrating because she may naturally express search goals rather than the topic keywords search engines need. In this paper, we present GOOSE (goal-oriented search engine), an adaptive search engine interface that uses natural language processing to parse a user’s search goal, and uses “common sense” reasoning to translate this goal into an effective query. For a source of common sense knowledge, we use Open Mind, a knowledge base of approximately 400,000 simple facts such as “If a pet is sick, take it to the veterinarian” garnered from a Web-wide network of contributors. While we cannot be assured of the robustness of the common sense inference, in a substantial number of cases, GOOSE is more likely to satisfy the user’s original search goals than simple keywords or conventional query expansion.


Search Engine Search Task Search Query Query Expansion Original Query 
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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  1. 1.
    Ask Jeeves, Inc..: Ask Jeeves home page. (2002).
  2. 2.
    Belkin, N.J.: Intelligent information retrieval: Whose intelligence? In: ISI’ 96: Proceedings of the Fifth International Symposium for Information Science. Konstanz: Universtaetsver-lag Konstanz. (1996). 25–31.Google Scholar
  3. 3.
    Klink, S.: Query reformulation with collaborative concept-based expansion. Proceedings of the First International Workshop on Web Document Analysis, Seattle, WA (2001).Google Scholar
  4. 4.
    Lieberman, H., Liu, H.: Adaptive Linking between Text and Photos Using Common Sense Reasoning. In Proceedings of the 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, Malaga, Spain (2002).Google Scholar
  5. 5.
    Minsky, M.: Commonsense-Based Interfaces. Communications of the ACM. Vol. 43, No. 8 (August, 2000), Pages 66–73CrossRefGoogle Scholar
  6. 6.
    Minsky. M.: A Framework for Representing Knowledge. MIT, (1974). Also, In: P.H. Winston (Ed.): The Psychology of Computer Vision., McGraw-Hill, New York, (1975).Google Scholar
  7. 7.
    Peat, H. J. and Willett, P.: The limitations of term co-occurrence data for query expansion in document retrieval systems. Journal of the ASIS, 42(5), (1991), 378–383.Google Scholar
  8. 8.
    Singh, P.: The Public Acquisition of Commonsense Knowledge. AAAI Spring Symposium, Stanford University, Palo Alto, CA, (2002).Google Scholar
  9. 9.
    Shneiderman, B., Byrd, D., and Croft, B.: Sorting out searching: A user-interface framework for text searches, Communications of the ACM 41, 4 (April 1998), 95–98.Google Scholar
  10. 10.
    Voorhees, E.: Query expansion usin lexical-semantic relations. In Proceedings of ACM SIGIR Intl. Conf. on Research and Development in Information Retrieval. (1994) 61–69.Google Scholar
  11. 11.
    Xu, J. and Croft, W.B.: Query Expansion Using Local and Global Document Analysis. In Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, (1996). pp. 4–11.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hugo Liu
    • 1
  • Henry Lieberman
    • 1
  • Ted Selker
    • 1
  1. 1.MIT Media LaboratoryCambridgeUSA

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