Evaluating Document Retrieval Methods for Resource Selection in Clustered P2P IR

Alkhawaldeh, R. S., Jose, J. M. and P, D. (2016) Evaluating Document Retrieval Methods for Resource Selection in Clustered P2P IR. In: 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN, USA, 24-28 Oct 2016, pp. 2073-2076. ISBN 9781450340731 (doi: 10.1145/2983323.2983912)

Abstract

Resource Selection (or Query Routing) is an important step in P2P IR. Though analogous to document retrieval in the sense of choosing a relevant subset of resources, resource selection methods have evolved independently from those for document retrieval. Among the reasons for such divergence is that document retrieval targets scenarios where underlying resources are semantically homogeneous, whereas peers would manage diverse content. We observe that semantic heterogeneity is mitigated in the clustered 2-tier P2P IR architecture resource selection layer by way of usage of clustering, and posit that this necessitates a re-look at the applicability of document retrieval methods for resource selection within such a framework. This paper empirically benchmarks document retrieval models against the state-ofthe-art resource selection models for the problem of resource selection in the clustered P2P IR architecture, using classical IR evaluation metrics. Our benchmarking study illustrates that document retrieval models significantly outperform other methods for the task of resource selection in the clustered P2P IR architecture. This indicates that clustered P2P IR framework can exploit advancements in document retrieval methods to deliver corresponding improvements in resource selection, indicating potential convergence of these fields for the clustered P2P IR architecture.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Alkhawaldeh, Mr Rami Suleiman
• Authors: Alkhawaldeh, R. S., Jose, J. M., and P, D.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450340731
• Copyright Holders: Copyright © 2016 ACM
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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