Intelligent Search for Distributed Information Sources Using Heterogeneous Neural Networks

  • Hui Yang
  • Minjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2642)


As the number and diversity of distributed information sources on the Internet exponentially increase, various search services are developed to help the users to locate relevant information. But they still exist some drawbacks such as the difficulty of mathematically modeling retrieval process, the lack of adaptivity and the indiscrimination of search. This paper shows how heterogeneous neural networks can be used in the design of an intelligent distributed information retrieval (DIR) system. In particular, three typical neural network models — Kohoren’s SOFM Network, Hopfield Network, and Feed Forward Network with Back Propagation algorithm are introduced to overcome the above drawbacks in current research of DIR by using their unique properties. This preliminary investigation suggests that Neural Networks are useful tools for intelligent search for distributed information sources.


Information Retrieval Information Source Feed Forward Network Query Term Hopfield Network 
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. 1.
    Belew, Y. K.: Adaptive Information Retrieval. Proceedings of the 12th International Conference on Research and Development in Information Retrieval. Cambridge, Massachusetts (1989) 11–20Google Scholar
  2. 2.
    Chen, H., Lynch, K. J., Basu, K. and Ng, D. T.: Generating, integrating, and activating thesauri for concept-based document retrieval, Int. J. IEEE EXPERT, Special Series on Artificial Intelligence in Text-based Information Systems, Vol. 8(2). (1993) 25–34Google Scholar
  3. 3.
    Crestani, F.: Learning strategies for an adaptive information retrieval system using neural networks. Proceedings of IEEE International Conference on Neural Networks, Vol. 1. (1993) 244–249CrossRefGoogle Scholar
  4. 4.
    Haykin, S.: Neural Networks: a comprehensive foundation. 2th edn. Upper Saddle Rever, New Jersey, Prentice Hall (1999)zbMATHGoogle Scholar
  5. 5.
    Hopfield, J. J.: Neural Network and physical systems with collective computational abilities. Proceedings of the National Academy of Sciences, Vol. 79(4). USA (1982) 2554–2558CrossRefMathSciNetGoogle Scholar
  6. 6.
    Huang, W. and Lippman, R.: Network Net and Conventional Classifier. Proceedings of IEEE Conference on Neural Information Processing System-Natural and Synthetic, Boulder, CO(1987)Google Scholar
  7. 7.
    Kohonen, T.: Self-Organization and Associative Memory. 2nd, edn. Springer-Verlag, Berlin (1988)zbMATHGoogle Scholar
  8. 8.
    Kwok, K. L.: A Neural Network for Probabilistic Information Retrieval. Proceedings of the 12th International Conference on Research and Development in Information Retrieval. Cambridge, Massachusetts (1989) 21–30Google Scholar
  9. 9.
    Mozer, M. C.: Inductive Information Retrieval using Parallel Distributed Computatio. Technical Report. ICS, UCSD, La Jolla, California (1984)Google Scholar
  10. 10.
    Muneesawang, P. and Guan, L.: A Neural Network Approach for learning Image Similarity in Adaptive CBIR. Proceedings of the IEEE Fourth Workshop on Multimedia Signal. (2001) 257–262Google Scholar
  11. 11.
    Salton, G. and Buckley, C.: On the use of spreading activation methods in automatic information retrieval. Proceedings of the 11th International Conference on Research & Development in Information Retrieval. New York (1988) 147–160Google Scholar
  12. 12.
    Turtle, H. and Croft, W. B.: Evaluation of an Inference Network-Based Retrieval Model. Int. J. ACM Transaction on Information System, Vol. 9(3). (1991) 187–222CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hui Yang
    • 1
  • Minjie Zhang
    • 1
  1. 1.School of Information Technology and Computer ScienceUniversity of WollongongWollongongAustralia

Personalised recommendations