Social contexts and the probabilistic fusion and ranking of opinions: Towards a social semantics for the Semantic Web

In the (Semantic) Web, the existence or producibility of certain, consensually agreed or authoritative knowledge cannot be assumed, and criteria to judge the trustability and reputation of knowledge sources may not be given. These issues give rise to the formalization of web information in terms of heterogeneous and possibly inconsistent public assertions and intentions, providing valuable meta-information in contemporary application fields, like open or distributed ontologies, social software, ranking and recommender systems, and domains with a high amount of controversies, such as politics and culture. As an approach towards this, we introduce a lean, intuitive formalism for the Semantic Web which allows for the explicit representation of semantic heterogeneity by means of so-called social contexts, and optionally for the probabilistic aggregation and social rating of possibly uncertain or contradictious assertions. Inter alia, this allows to stochastically generalize multiple assertions (yielding complexity reduction), and generalizes the concept of folksonomies to any ontologies and description logic knowledge bases emergent from social choice processes.