Exploiting Social Property for Improving Distributed Semantic Search

To locate desirable Semantic Web data in a distributed network, the discovering mechanism has to be not only semantically rich, in order to cope with complex queries, but also scalable to handle large numbers of information sources. In this paper, we propose a novel scheme that exploits the social property of humans, such as natural grouping and peer recommendation between people with common interests, to expedite the discovery of Semantic Web data in large-scale distributed networks. In this framework, network nodes perform local dynamic topology adaptations to spontaneously create communities according to users’ social-closeness. The basic premise of such semantic communities is that search requests have a high probability of being fulfilled within the community they originate from. For queries which cannot be efficiently solved inside the community, an index overlay built on Distributed Hash Table (DHT) is used to assist the search. Recommendations from peers with similar interests are employed to improve both the efficiency and the precision of the semantic search. Experiments with simulations substantiate that our techniques significantly improve the search efficiency, scalability, and precision.