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The Peer-to-Peer systems (P2P) are the major technology of access upon various resources on Internet. A fundamental problem in Peer-to-Peer networks is how to locate appropriate peers efficiently to answer a specific query (Query routing). This paper proposes a semantic model in , which a query can be routed for appropriate peers instead of broadcasting or using random selection. This semantic is generally built from the contents of the peers, but can also bring in the implicit behavior of the users. The main objective of this model is to achieve better results in non-supervised tasks through the incorporation of usage data obtained from past search queries. This type of model allows us to discover the motivations of users when visiting a certain documents and peers. The terms used in past queries can provide a better choice of features queries. Hence, for each peer, our model learns from past queries to represent correlation between queries terms, documents and peers. We implemented the proposed strategy, and compared its routing effectiveness in terms of both recall and messages traffic with a broadcasting scheme (without learning). To test the proposed algorithm, we defined a layer of routing on the PeerSim simulator. Experimental results show the model is efficient and performs better than other non-semantic query routing models with respect to accuracy. In addition, our approach improves the recall rate nearly 90% while reducing message traffic dramatically compared with Gnutella protocol.