Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering

Social networks are a popular movement on the web. On the Semantic Web, it is simple to make trust 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 an application, FilmTrust, that combines the computed trust values with the provenance of other annotations to personalize the website. The FilmTrust system uses trust to compute personalized recommended movie ratings and to order reviews. We believe that the results obtained with FilmTrust illustrate the success that can be achieved using this method of combining trust and provenance on the Semantic Web.