A Semantic Searching Scheme in Heterogeneous Unstructured P2P Networks |
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Authors: | Jun-Cheng Huang Xiu-Qi Li Jie Wu |
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Affiliation: | (1) Shanghai Hewlett-Packard Co., Ltd.,, No. 889 Yishan Road Xuhui, Shanghai, 201206, China;(2) Department of Computer Science and Mathematics, University of North Carolina at Pembroke, Pembroke, U.S.A.;(3) Department of Computer and Information Sciences, Temple University, Philadelphia, U.S.A. |
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Abstract: | Semantic-based searching in peer-to-peer (P2P) networks has drawn significant attention recently. A number of semantic searching
schemes, such as GES proposed by Zhu Y et al., employ search models in Information Retrieval (IR). All these IR-based schemes use one vector to summarize semantic contents
of all documents on a single node. For example, GES derives a node vector based on the IR model: VSM (Vector Space Model). A topology adaptation algorithm and a search protocol are then designed
according to the similarity between node vectors of different nodes. Although the single semantic vector is suitable when
the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there
are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES
and present a new class-based semantic searching scheme (CSS) specifically designed for unstructured P2P networks with heterogeneous
single-node document collection. It makes use of a state-of-the-art data clustering algorithm, online spherical k-means clustering (OSKM), to cluster all documents on a node into several classes. Each class can be viewed as a virtual node.
Virtual nodes are connected through virtual links. As a result, the class vector replaces the node vector and plays an important
role in the class-based topology adaptation and search process. This makes CSS very efficient. Our simulation using the IR
benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision, and lower search
cost. |
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