Distributed Information Retrieval on the Internet

Abstract

In this work, we propose a novel approach, a neural approach, to the text database discovery problem. First, we present a neural agent that learns about underlying text databases from the user's relevance feedback. For a given query, the neural agent, which is sufficiently trained on the basis of the neural net learning mechanism, discovers the text databases associated with the relevant documents and retrieves those documents effectively. Finally, we evaluate the performance of our approach by comparing it to those of the conventional well-known approaches.