On the use of spreading activation methods in automatic information
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
A neural network for probabilistic information retrieval
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of an inference network-based retrieval model
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Generating, integrating, and activating thesauri for concept-based document retrieval
IEEE Expert: Intelligent Systems and Their Applications
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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.