Peer-to-Peer systems have proven to be an effective way of sharing data. Modern protocols are able to efficiently route a message to a given peer. However, determining the destination peer in the first place is not always trivial. Especially in a distributed knowledge management setting [8] , knowing the right peers for answering a given query can be difficult. 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 topology. 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. For our similarity measure we assume that the peers share the same ontology. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments we show how expertise based peer selection improves the performance of a Peer-toPeer system with respect to criteria such as precision, recall and the number of messages.
A peer knows about the expertise topics from other peers by analyzing advertisement messages REF or answers (chapter 5).
Peer Selection in Peer-to-Peer Networks with Semantic Topologies
ICSNW
Computer Science