Expertise-Based Peer Selection

Peer-to-Peer systems have proven to be an effective way of sharing data. Finding the data in an efficient and robust manner still is a challenging problem. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic overlay network (SON). Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments complemented with a real-world field experiment we show how expertise based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages.