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Intelligent Search for Distributed Information Sources Using Heterogeneous Neural Networks

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

Abstract

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.

Keywords

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.

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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

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