Abstract
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. 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. To calculate our semantic similarity measure we make the simplifying assumption 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-to-Peer system with respect to precision, recall and the number of messages.
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Keywords
- Semantic Similarity
- Latent Semantic Indexing
- Expertise Model
- Topic Distribution
- Semantic Similarity Measure
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Haase, P., Siebes, R., van Harmelen, F. (2004). Peer Selection in Peer-to-Peer Networks with Semantic Topologies. In: Bouzeghoub, M., Goble, C., Kashyap, V., Spaccapietra, S. (eds) Semantics of a Networked World. Semantics for Grid Databases. ICSNW 2004. Lecture Notes in Computer Science, vol 3226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30145-5_7
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DOI: https://doi.org/10.1007/978-3-540-30145-5_7
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