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.
Peer-to-Peer Information retrieval Semantic similarity
KUN-PENG ZHU ZHI-MING XU XIAO-LONG WANG YU-MING ZHAO
Intelligent Technology and Natural Language Processing Lab, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
4038-4043
2007-08-19（万方平台首次上网日期，不代表论文的发表时间）