Lexical navigation: Using incremental graph drawing for query refinement

  • Daniel Tunkelang
  • Roy J. Byrd
  • James W. Cooper
Systems I
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1353)


Query refinement is a powerful tool for a document search and retrieval system. Lexical navigation—that is, the exploration of a network that expresses relations among all possible query terms—provides a natural mechanism for query refinement. An essential part of lexical navigation is the visualization of this network. This dynamic visualization problem is essentially one of incrementally drawing and manipulating a non-hierarchical graph. In this paper, we present the graph-drawing system we have developed for lexical navigation.


  1. [1]
    Bates, Marcia J. “Human, Database, and Domain Factors in Content Indexing and Access to Digital Libraries and the Internet,” Allerton, 1996Google Scholar
  2. [2]
    Cooper, James W. and Byrd, Roy J., “Lexical Navigation: Visually Prompted Query Expansion and Refinement” in Proceedings of the 2 nd ACM International Conference on Digital Libraries, July 1997.Google Scholar
  3. [3]
    DeJesus, Edmund X., “The Searchable Kingdom” in Byte, June 1997.Google Scholar
  4. [4]
    Di Battista, Giuseppe et al., “Annotated Bibliography on Graph Drawing Algorithms” in Computational Geometry: Theory and Applications 4, 1994.Google Scholar
  5. [5]
    Eades, Peter, “A Heuristic for Graph Drawing,” in Congressus Numerantium 42, 1984.Google Scholar
  6. [6]
    Fowler, Richard H., Wilson, Bradley A., and Fowler, Wendy A. L. “Information Navigator: An information system using associative networks for display and retrieval”, Report NAG9-551, No, 92-1, Dept of Computer Science, University of Texas-Pan American, Edinburg, TX.Google Scholar
  7. [7]
    Furnas, G. W, Landauer, T. K., Gomes, L. M., and Dumais, S. T. “The Vocabulary Problem in Human-System Communication,” in Communications of the ACM, vol. 30, no. 11, November 1987, pp. 964–971.CrossRefGoogle Scholar
  8. [8]
    Harman, D. “Relevance Feedback and Other Query Modification Techniques,” in W. B. Frakes and R. Baeza-Yates, eds., Information Retrieval: Data Structures and Algorithms, Prentice-Hall, 1992.Google Scholar
  9. [9]
    NIST TIPSTER Information-Retrieval Test Research Collection, on CD-ROM, published by the National Institute of Standards and Technology, Gaithersburg, MD, 1993.Google Scholar
  10. [10]
    North, Stephen C., “Incremental Layout in DynaDAG,” in Proceedings of Symposium on Graph Drawing, 1995.Google Scholar
  11. [11]
    Schatz, Bruce R., Johnson, Eric H., Cochrane, Pauline A., and Chen, Hsinchun,“ Interactive Term Suggestion for Users of Digital Libraries,” in Proceedings of ACM Digital Libraries Conference, 1996.Google Scholar
  12. [12]
    Sugiyama, Kozo et al., “Methods for Visual Understanding of Hierarchical Systems” in IEEE Transactions on Systems, Man, and Cybernetics 11, No. 2, 1981.Google Scholar
  13. [13]
    Tom Sawyer, on the World-Wide Web at http://www.tomsawyer.comGoogle Scholar
  14. [14]
    Tunkelang, Daniel, “A Practical Approach to Drawing Undirected Graphs”, Technical Report CMU-CS-94-161, Carnegie Mellon University, June 1994.Google Scholar
  15. [15]
    Tunkelang, Daniel and Wegman, Mark, “Applying Numerical Approximation to Graph Drawing,” unpublished manuscript. To request, please send email to quixote@cmu.edu.Google Scholar
  16. [16]
    Visual LiveTopics, on the World-Wide Web at htlp://www.altavista.digital.com.Google Scholar
  17. [17]
    Voorhees, E. M. “Query Expansion using Lexical-Semantic Relations,” in Proceedings of the 17th Annual ACM-SIGIR Conference, 1994, pp. 61–69.Google Scholar
  18. [18]
    Xu, Jinxi and Croft, W. Bruce “Query Expansion Using Local and Global Document Analysis,” Proceedings of the 19th Annual ACM-SIGIR Conference, 1996, pp. 4–11.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Daniel Tunkelang
    • 1
  • Roy J. Byrd
    • 2
  • James W. Cooper
    • 2
  1. 1.School of Computer ScienceCarnegie Mellon UniversityUSA
  2. 2.IBM T. J. Watson Research CenterUSA

Personalised recommendations