A peer-to-peer information retrieval system based on semantic similarity model

Peer-to-Peer (P2P) networks have recently received more and more attention from researchers. P2P seems to be an interesting architectural paradigm for realizing large-scale information retrieval systems for its scalability, failure resilience and increased autonomy of nodes. This paper provides a novel Peer-to-Peer networks system that is based on information retrieval in a large-scale collection of texts, and a semantic similarity model is developed and applied in it, which improves the performance of the system. Some natural language processing technologies are adopted to increase the accuracy of the system. Several useful tools are incorporates as external auxiliary resources. In addition, feedback knowledge such as query information from Peers is also widely used to direct querying messages flooding based on a semantic routing mechanism in this system. Finally, an experimental study is used to verify the advantages of system, and the results are comparatively satisfying.