ULYSSES: A lattice-based multiple interaction strategy retrieval interface

  • Claudio Carpineto
  • Giovanni Romano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1015)


We present a two-stage system for information exploration and retrieval. The first stage, named GALOIS, organizes the information contained into a database into a particular lattice structure; the second stage, named ULYSSES, is a visual interface to access the structure built in the earlier stage. In this paper we focus on the latter. ULYSSES is based on a tight integration of traditional and novel user interaction paradigms that can be seen as a search&bound approach to information retrieval. The user may search the retrieval space by browsing or querying, but he or she may also bound the retrieval space by specifying constraints that the information contained in it has to satisfy. These interaction modes can be naturally combined to produce a hybrid retrieval strategy that best reflects the user goals and his/her domain knowledge. The retrieval effectiveness of our system has been tested in an experiment on subject searching where it compared favourably with respect to a Boolean retrieval system.


Information Retrieval Interaction Mode Retrieval Performance Retrieval Effectiveness User Goal 
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. Ahlberg, C., Shneiderman, B. (1994). Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. In Proceedings of CHI'94 (pp. 313–317), Boston, MA: Addison Wesley.Google Scholar
  2. Balas, E. (1968). “A note on the branch-and-bound principle”, Operations Research, 16, 442–444.Google Scholar
  3. Bowman, M., Danzig, P., Manber, U., & Schwartz, F. (1994). Scalable Internet Resource Discovery: Research Problems and Approaches. Communications of the ACM, 37, 8, pp. 98–114.Google Scholar
  4. Carpineto, C., & Romano, G. (1993). GALOIS: An order-theoretic approach to conceptual clustering. In Proceedings of the 10th International Conference on Machine Learning (pp. 33–40), Amherst, MA:Morgan Kaufmann.Google Scholar
  5. Carpineto, C., & Romano, G. (1994a). A lattice conceptual clustering system and its application to browsing retrieval. Submitted to Machine Learning.Google Scholar
  6. Carpineto, C., & Romano, G. (1994b). Dynamically bounding browsable retrieval spaces: an application to Galois lattices. In Proceedings of RIAO 94: Intelligent Multimedia Information Retrieval Systems and Management(pp. 520–533), New York.Google Scholar
  7. Davey, B., & Priestley, H. (1990). Introduction to Lattices and Order. Cambridge, Great Britain: Cambridge University Press.Google Scholar
  8. Eades, P., & Tamassia, R. (1989). Algorithms for drawing graphs: An annotated bibliography. Tech. Rep. CS-89-90, Dept. of Computer Science, Brown Univ., Providence, R.I..Google Scholar
  9. Frei, H., Jauslin, J. (1983). Graphical presentation of information and services: a user oriented interface. Information Technology: Research and Development, 2, 23–42.Google Scholar
  10. Furnas, G. (1986). Generalized fisheye views. Proceedings of the Human Factors in Computing Systems (pp. 16–23). North Holland.Google Scholar
  11. Godin, R., Missaoui, R., & April, A. (1993). Experimental comparison of navigation in a Galois lattice with conventional information retrieval methods. International Journal of Man-machine Studies, 38, 747–767.Google Scholar
  12. Kaplan, C., Fenwick, J., & Chen, J. (1993). Adaptive Hypertext Navigation Based On User Goals and Context. User Modeling and User-Adapted Interaction, 3, 193–220.Google Scholar
  13. Kumar, V. (1984). A General Bottom-up Procedure for Searching AND/OR Graphs. Proceedings of the Fourth National Conference on Artificial Intelligence, Austin, TX.Google Scholar
  14. Lucarella, D., Parisotto, S., Zanzi, A. (1993). MORE: Multimedia Object Retrieval Evironment. Proceedings of the Fifth ACM Conference on Hypertext (pp. 39–50). Seattle, WA.Google Scholar
  15. Maarek, Y., Berry, D., & Kaiser, G. (1991). An Information Retrieval Approach For Automatically Constructing Software Libraries. IEEE Transactions on Software Engineering, 17, 8, 800–813.Google Scholar
  16. Marchionini, G., & Shneiderman, B. (1988). Finding facts vs. browsing knowledge in hypertext systems. IEEE Computer, 21, 70–80.Google Scholar
  17. Mitchell, T. (1982). Generalization as search. Artificial Intelligence, 18, 203–226.Google Scholar
  18. Nielsen, J. (1990). Hypertext & hypermedia. San Diego, CA: Academic Press.Google Scholar
  19. Pedersen, G. (1993). A browser for bibliographic information retrieval based on an application of lattice theory. Proceedings of SIGIR'93 (pp. 270–279), Pittsburgh, PA.Google Scholar
  20. Rao, R., Russel, D, & Mackinlay, J. (1993). System Components for Embedded Information Retrieval from Multiple Disparate Information Sources. Proceedings of the ACM Symposium on User Interface Software and Technology (pp. 23–33), Atlanta, Georgia.Google Scholar
  21. Rivlin, E., Botafogo, R., Shneiderman, B. (1994). Navigating in Hyperspace: Designing a Structure-based Toolbox. Communications of the ACM, 37, 2, 87–96.Google Scholar
  22. Salton, G., Buckley, C. (1990). Improving retrieval performance by relevance feedback. JASIS, 41, 288–297.Google Scholar
  23. Salton, G., & McGill, M. (1983). Introduction to modern information retrieval. New York: McGraw Hill.Google Scholar
  24. Sarkar, M., & Brown, M. (1994). Graphical Fisheye Views. Communications of the ACM, 37, 12, 73–84.Google Scholar
  25. Shneiderman, B. (1987). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Reading, MA: Addison Wesley.Google Scholar
  26. Tague-Sutcliffe, J. (1992). The pragmatics of information retrieval experimentation, revisited. Information Processing & Management, 28, 4, 467–490.Google Scholar
  27. Thompson, R., & Croft, B. (1989). Support for browsing in an intelligent text retrieval system. International Journal of Man-machine Studies, 30, 639–668.Google Scholar
  28. van Rijsbergen (1975). Information retrieval. London: Butterworths.Google Scholar
  29. Williamson, C., Shneiderman, B. (1992). The Dynamic Homefinder: Evaluating Dynamic Queries in a Real-Estate Information Exploration System. In Proceedings of SIGIR'92 (pp. 338–346); New York: ACM SIGIR Forum.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Claudio Carpineto
    • 1
  • Giovanni Romano
    • 1
  1. 1.Fondazione Ugo BordoniRomeItaly

Personalised recommendations