Semantic-based query routing in P2P social networks

Abstract

In P2P social networks, query efficiency is always a significant challenge. In this paper we build a semantic-based query routing strategy to improve the searching efficiency. Based on Vector Space Model (VSM), similar semantic ordinary peers set up an unstructured semantic community. Super peers in each community constitute a structured P2P network and store the keywords information in a global inverted file. We exploit Bloom Filters to complete multi-keyword query. Simulation results show that the proposed scheme is able to increase query efficiency and also reduce query cost.