Improving Searching Performance Based on Semantic Correlativity in Peer to Peer Network

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 self-clustering. 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.

• Type: Conference
• Year: 2005
• Where: SKG
• Authors: Zhichao Li, Pilian He, Feng Li, Ming Lei
• Keywords: Routing Performance, Semantic Content-based Searches, Semantic Correlativity Model, SKG 2005