Abstract
In this paper, we propose a novel framework for discovery Semantic Web data in large-scale distributed networks. In this framework, peers dynamically perform topology adaptations to spontaneously create communities with similar semantic interests, so that search requests have a high probability of being satisfied within the local community. For queries which cannot be efficiently solved inside the community, a directory overlay built on Distributed Hash Table (DHT) is used to assist the search. Recommendation from peers of the same community is employed to extract only semantically related results thus improving the precision. Experiments with simulations substantiate that our techniques significantly improve the search efficiency, scalability, and precision.
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Li, J. (2009). Improving Distributed Semantic Search with Hybrid Topology and Peer Recommendation. In: Lee, R., Hu, G., Miao, H. (eds) Computer and Information Science 2009. Studies in Computational Intelligence, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01209-9_8
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DOI: https://doi.org/10.1007/978-3-642-01209-9_8
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