Using trust and provenance for content filtering on the semantic web

Abstract

Social networks are a popular movement on the web. Trust can be used effectively on the Semantic Web as annotations to social relationships. In this paper, we present a two level approach to integrating trust, provenance, and annotations in Semantic Web systems. We describe an algorithm for inferring trust relationships using provenance information and trust annotations in Semantic Web-based social networks. Then, we present two applications that combine the computed trust values with the provenance of other annotations to personalize websites. The FilmTrust system uses trust to compute personalized recommended movie ratings and to order reviews. An open source intelligence portal, Profiles In Terror, also has a beta system that integrates social networks with trust annotations. We believe that these two systems illustrate a unique way of using trust annotations and provenance to process information on the Semantic Web.