A Computational Trust Model for Semantic Web Based on Bayesian Decision Theory

Enabling trust to ensure more effective and efficient agent interaction is at the heart of the Semantic Web vision. We propose a computational trust model based on Bayesian decision theory in this paper. Our trust model combines a variety of sources of information to assist users with making correct decision in choosing the appropriate providers according to their preferences that expressed by prior information and utility function, and takes three types of costs (operational, opportunity and service charges) into account during trust evaluating. Our approach gives trust a strict probabilistic interpretation and lays solid foundation for trust evaluating on the Semantic Web.