Skip to main content

Neural navigation interfaces for Information Retrieval: Are they more than an appealing idea?

Abstract

Neural networks have recently been proposed for the construction of navigation interfaces for Information Retrieval systems. In this paper, we give an overview of some current research in this area. Most of the cited approaches use (variants) of the well-known Kohonen network. The Kohonen network implements a topology-preserving dimensionality-reducing mapping, which can be applied for information visualization. We identify a number of problems in the application of Kohonen networks for Information Retrieval, most notably scalability, reliability and retrieval effectiveness. To solve these problems we propose to use the Growing Cell Structures network, a variant of the Kohonen network which shows a more flexible adaptation to the domain structure.

This network was tested on two standard test-collections, using a combined recall and precision measure, and compared to traditional IR methods such as the Vector Space Model and various clustering algorithms. The network performs at a competitive level of effectiveness, and is suitable for visualization purposes. However, the incremental training procedures for the networks result in a reliability problem, and the approach is computationally intensive. Also, the utility of the resulting maps for navigation will need further improvement.

This is a preview of subscription content, access via your institution.

References

  1. Blackmore, J. & Miikkulainen, R. (1993). Incremental Grid Growing: Encoding High Dimensional Structure into a Two-Dimensional Feature Map, In Proc. of the 1993 IEEE International Conference on Neural Networks, 450–455. San Francisco, CA.

  2. Chalmers, M. & Chitson, P. (1992). Bead: Explorations in Information Visualisation. In Proc. of the 15th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, 330–337. Copenhagen, Denmark.

  3. Chalmers, M. (1995). Design Perspectives in Visualising Complex Information. In Proc.IFIP 3rd Visual Databases Conference. Lausanne, Switzerland.

  4. Can, F. & Ozkarahan, E. A. (1984). Two Partitioning Type Clustering Algorithms. Journal of the American Society for Information Science 35(5): 268–276.

    Google Scholar 

  5. Cutting, D. R., Karger, D. R., Pedersen, J. O. & Tukey, J. W. (1992). Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections, In Proc. of the 15th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, 262–269. Copenhagen, Denmark.

  6. De Heer, T. (1982). The Application of the Concept of Homeosemy to Natural Language Information Retrieval. Information Processing and Management 18(5): 229–236.

    Google Scholar 

  7. Doszkocs, T.E., Reggia, J. & Lin X. (1990). Connectionist Models and Information Retrieval. Annual Review of Information Science and Technology, 25. American Society for Information Science.

  8. Doyle, L. B. (1961). Semantic Road Maps for Literature Searchers. Journal of the ACM 8: 553–578.

    Google Scholar 

  9. Everitt, B. S. (1993). Cluster Analysis, 3rd edition. Halsted Press: New York.

    Google Scholar 

  10. Fowler, R. H., Fowler, W. A. L. & Wilson, B.A. (1991). Integrating Query, Thesaurus, and Documents through a Common Visual Representation. In Proc. of the 14th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, 142–151. Chicago, IL.

  11. Fritzke, B. (1994). Growing Cell Structures—A Self-Organizing Network for Unsupervised and Supervised Learning. Neural Networks 7(9): 1441–1460.

    Google Scholar 

  12. Furnas, G. W., Deerwester, S., Dumais, S. T., Landauer, T. K., Harshman, R. A., Streeter, L. A. & Lochbaum, K. E. (1988). Information Retrieval using a Singular Value Decomposition Model of Latent Semantic Structure. In Proc. of the 11th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, 465–480.

  13. Griffiths, A., Luckhurst, H. C. & Willett, P. (1986). Using Interdocument Similarity Information in Document Retrieval Systems. Journal of the American Society for Information Science 37(1): 3–11.

    Google Scholar 

  14. Hecht-Nielsen, R. (1990). Neurocomputing. Addison-Wesley.

  15. Hemmje, M., Kunkel, C. & Willett, A. (1994). LyberWorld — A Visualization User Interface Supporting Fulltext Retrieval. In Proc. of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, 249–259. Dublin, Ireland.

  16. Hertz, J. A., Krogh, A. S. & Palmer, R. G. (1991). Introduction to the Theory of Neural Computation. Addison-Wesley.

  17. Kohnen, T. (1990). The Self-Organizing Map. Proc. of the IEEE 78: 1464–1480.

    Google Scholar 

  18. Korfhage, R. R., Nuchprayoon, A. & Parmanto B. (1995). Structured Display and Browsing of Documentary Information. Integrated Computer-Aided Engineering 2(1).

  19. Lelu, A. (1991). From Data Analysis to Neural Networks: New Prospects for Efficient Browsing Through Databases. Journal of Information Science 17: 1–12.

    Google Scholar 

  20. Lin, X., Soergel, D. & Marchioni, G. (1991). A Self-Organizing Semantic Map for Information Retrieval. Proc. of the 14th Annual International ACM-SIGIR Conference on Researchand Development in Information Retrieval, 262–269. Chicago, IL.

  21. Lin, X. (1995). Searching and Browsing on Map Displays. unpublished manuscript, available through URL: http: www. uky. edu/≈xlin/asis95. htm.

  22. MacLeod, K. J. & Robertson, W. (1991). A Neural Algorithm for Document Clustering. Information Processing and Management 27(4): 337–346.

    Google Scholar 

  23. Ritter, H., Martinetz, T. & Schulten, K. (1992). Neural Computation and Self-Organizing Maps: An Introduction. Addison-Wesley.

  24. Rozmus, J. M. (1995). Information Retrieval by Self-Organizing Maps. Proc. of the 1995 National Online Meeting, Learned Information Inc., Medford, NJ.

  25. Salton, G. (1989). Automatic Text Processing: the Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley.

  26. Scholtes, J. C. (1993). Neural Networks in Natural Language Processing and Information Retrieval, Ph.D. diss., Dept. of Computational Linguistics, Universiteit van Amsterdam.

  27. Scholtes, J. C. (1993). Artificial Neural Networks for Information Retrieval in a Libraries Context. State-of-the-Art Report, EC/PROLIB/ANN Contract, MSC Information Retrieval Technologies BV, Amsterdam.

    Google Scholar 

  28. Van Rijsbergen, C. J. (1979). Information Retrieval. Butterworths: London.

    Google Scholar 

  29. VIRI (1995). Visual Information Retrieval Interfaces. www page at URL: http: www-cui. darmstadt. gmd. de: 80/visit/Activities/Viri/visual. html

  30. Willett, P. (1988). Recent Trends in Hierarchical Document Clustering: A Critical Review. Information Processing and Management 24(5): 577–597.

    Google Scholar 

  31. Zavrel, J. (1995), Neural Information Retrieval: An Experimental Study of Clustering and Browsing of Document Collections with Neural Networks. MA Thesis, Universiteit van Amsterdam, available through URL: ftp: itkftp. kub. nl/pub/Jakub/zavrel. scriptie. ps. Z

Download references

Author information

Affiliations

Authors

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zavrel, J. Neural navigation interfaces for Information Retrieval: Are they more than an appealing idea?. Artif Intell Rev 10, 477–504 (1996). https://doi.org/10.1007/BF00130695

Download citation

Key words

  • information retrieval
  • information visualization
  • semantic road maps
  • clustering
  • neural networks
  • self-organizing maps
  • evaluation