Applying and Inferring Fuzzy Trust in Semantic Web Social Networks

Abstract

Social networks let the people find and know other people and benefit form their information. Semantic Web standard ontologies support social network sites for making use of other social networks information and hence help their expansion and unification, making them a huge social network. As social networks are public virtual social places much information may exist in them that may not be trustworthy to all. A mechanism in needed to rate coming news, reviews and opinions about a definite subject from users, according to each user preference. There should be a feature for users to specify how much they trust a friend and a mechanism to infer the trust from one user to another that is not directly a friend of the user so that a recommender site can benefit from these trust ratings for showing trustworthy information to each user from her or his point of view from not only her or his directly trusted friends but also the other indirectly trusted users. This work suggests using fuzzy linguistic terms to specify trust to other users and proposes an algorithm for inferring trust from a person to another person that may be not directly connected in the trust graph of a social network. The algorithm is implemented and compared to an algorithm that let the users to specify their trust with a number in a definite range. While according to the imprecise nature of the trust concept writing and reading a linguistic expression for trust is much more natural than a number for users, the results show that the algorithm offers more precise information than the previously used algorithm especially when contradictory beliefs should be composed and also when a more precise inference is potentially possible in searching deeper paths. As the trust graphs and inference are viewed abstractly, they can be well employed in other multi agent systems.