Toward Peer-to-Peer Based Semantic Search Engines : An Organizational Approach

Haizheng Zhang

ISBN 10: 3639084799 / ISBN 13: 9783639084795

Published by VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K., 2008

About this Item

Neuware - This book frames a peer-to-peer information retrievalproblem as a multi-agent framework and attacks itfrom an organizational perspective by exploringvarious adaptive, self-organizing topologicalorganizations, designing appropriatecoordination strategies, and exploiting learningtechniques to create more accurate routing policy forlarge-scale agent organizations. In addition, areinforcement-learning based approach is developed inthis thesis to take advantage of the run-timecharacteristics of P2P IR systems, includingenvironmental parameters, bandwidth usage, andhistorical information about past search sessions. Inthe learning process, agents refine their contentrouting policies by constructing relatively accuraterouting tables based on a Q-learning algorithm.Experimental results show that this learningalgorithm considerably improves the performance ofdistributed search sessions in P2P IR systems.The book is addressed to researchers andpractitioners in information retrieval and searchengine, content-based routing areas.

Seller Inventory # 9783639084795

Bibliographic Details

• Title: Toward Peer-to-Peer Based Semantic Search Engines : An Organizational Approach
• Publisher: VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K.
• Publication Date: 2008
• Language: English
• ISBN 10: 3639084799
• ISBN 13: 9783639084795
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book frames a peer-to-peer information retrieval problem as a multi-agent framework and attacks it from an organizational perspective by exploring various adaptive, self-organizing topological organizations, designing appropriate coordination strategies, and exploiting learning techniques to create more accurate routing policy for large-scale agent organizations. In addition, a reinforcement-learning based approach is developed in this thesis to take advantage of the run-time characteristics of P2P IR systems, including environmental parameters, bandwidth usage, and historical information about past search sessions. In the learning process, agents refine their content routing policies by constructing relatively accurate routing tables based on a Q-learning algorithm. Experimental results show that this learning algorithm considerably improves the performance of distributed search sessions in P2P IR systems. The book is addressed to researchers and practitioners in information retrieval and search engine, content-based routing areas.