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 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.

About the Author

Dr. Haizheng Zhang is currently a technical staff member atMicrosoft Live Search. Prior to Microsoft, he was a researchassociate at Pennsylvania State University. He received his Ph.D.on Computer Science from University of Massachusetts Amherst in2006. His research focus is on information retrieval, socialnetworks, and machine learning.