An effective approach for accurate estimation of trust of distant information sources in the semantic Web

To assess the trustworthiness of the information published in the World Wide Web referrals are often employed. This is due to the fact that most information sources are visited only occasionally by the same client, and thus, direct own experience rarely suffices. The accuracy of trust inference for unknown information sources may considerably deteriorate due to "noise" or to the intervention of malicious nodes producing and propagating untrustworthy referrals. In this paper, we describe an innovative approach for trust inference in the semantic Web and in trust networks in general, referred to as FACiLE (faith assessment combining last edges). Unlike all other approaches, FACilE infers a trust value for an information source from a proper combination of only the direct trust values of its neighbours. We evaluate the efficiency of our approach by means of a series of simulation experiments run for a wide variety of mixes of sources of untrustworthy information. FACiLE outperforms other trust-inference approach in the most interesting cases of population mixes