Intelligent search for distributed information sources using heterogeneous neural networks

Yang, Hui and Zhang, Minjie (2003). Intelligent search for distributed information sources using heterogeneous neural networks. In: Zhou, Xiaofang; Zhang, Yanchun and Orlowska, Maria E. eds. Web Technologies and Applications, 5th Asian-Pacific Web Conference (APWeb 2003). Lecture Notes in Computer Science, 2642. UK: Springer, pp. 513–524.

DOI: https://doi.org/10.1007/3-540-36901-5_52

URL: http://www.informatik.uni-trier.de/~ley/db/conf/ap...

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 heteroge-neous neural networks can be used in the design of an intelligent distributed in-formation 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.