Dealing with Uncertainty in the Semantic Web

Standardizing the Semantic Web is still an ongoing process. For some aspects, the standardization seems to have completed. For example, the syntax layer, the RDF data model layer and the RDFS and OWL semantic extensions have proven to fulfill their purpose in real world applications. Other aspects, while necessary to realize the greater ideal of the Semantic Web, are yet to be standardized. One of these is dealing with uncertainty. Like classical logic, the languages of the Semantic Web (RDF, RDFS and OWL) work under the assumption that knowledge is certain. Many forms of knowledge, e.g. in computer vision, computational linguistics and information retrieval, exhibit notions of uncertainty. Uncertainty also arises as a side effect of knowledge integration and ontology mapping. This thesis describes an extension for the Semantic Web to deal with uncertainty. The extension, called URDF (Uncertain RDF), extends RDF with the capability to express uncertainty by allowing to associate RDF formulas with probabilities. It not only extends RDF, but also supports the semantics of RDFS and part of OWL. The main contribution is an extension that adheres to the incremental design of the Semantic Web language stack. It can act as a unifying framework for different kinds of probabilistic representation and reasoning, at different levels of expressivity (RDF, RDFS or OWL). In this thesis, we focus on two kinds of reasoning: rule based reasoning with RDFS/OWL knowledge and Bayesian networks and inference.