First International Conference on Semantics, Knowledge and Grid (SKG'05)
Improving Searching Performance Based on Semantic Correlativity in Peer to Peer Network
Beijing, China
November 27-November 29
ISBN: 0-7695-2534-2
Zhichao Li, Tianjin University, Tianjin 300072, China
Pilian He, Tianjin University, Tianjin 300072, China
Feng Li, Baicheng Works University, Baicheng, China
Ming Lei, Tianjin University, Tianjin 300072, China
Most existing Peer-to-peer (P2P) systems support only title-based searches, which can not satisfy the content searches. In this paper, we proposed a semantic correlativity model which can support semantic content-based searches. Firstly, using VSM to represent content and using KNN algorithm to implement selfclustering. Secondly, based on framework, accessing to compute semantic similarity, SCRA policy is proposed to improve routing performance with prefetch technology. By this model, routing overhead can be greatly reduced. At last, preliminary simulation results show that SCRA achieves a great routing performance over the previous algorithms.
Citation:
Zhichao Li, Pilian He, Feng Li, Ming Lei, "Improving Searching Performance Based on Semantic Correlativity in Peer to Peer Network," skg, pp.20, First International Conference on Semantics, Knowledge and Grid (SKG'05), 2005
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb