EDUTELLA: a P2P networking infrastructure based on RDF
Proceedings of the 11th international conference on World Wide Web
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
KAON - Towards a Large Scale Semantic Web
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
The chatty web: emergent semantics through gossiping
WWW '03 Proceedings of the 12th international conference on World Wide Web
Routing Indices For Peer-to-Peer Systems
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Remindin': semantic query routing in peer-to-peer networks based on social metaphors
Proceedings of the 13th international conference on World Wide Web
Semantic resource management for the web: an e-learning application
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Efficient peer-to-peer keyword searching
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Searching dynamic communities with personal indexes
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.