earticle

영어

In unstructured P2P systems, peers organize themselves into a random overlay. A challenging problem in these systems is to efficiently locate appropriate peers to answer a specific query. This paper proposes a semantic method 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 our method is to achieve better results in non-supervised tasks through the incorporation of usage data obtained from past search queries. This type of method 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 method learns from past queries to represent correlation between sent queries terms and related peers. We implemented the proposed method, and compared its routing effectiveness in terms of both recall and messages traffic with a broadcasting scheme (without learning). Experimental results show that our method is efficient and performs better than other non-semantic query routing methods with respect to accuracy. In addition, our approach improves the recall rate nearly 90% while reducing message traffic dramatically compared with Gnutella protocol.