Abstract. The very simple structure of the RDF data model and semantics can lead to a number of issues when more complex scenarios are supposed to be represented in RDF. Aspects such as temporary evolution of a knowledge base, relevance and trust require more than just a model that consists of a set of universally true statements, without any reference to a situation, a point in time, or generally a context. Our proposed solution is to use the notion of context to separate statements that refer to different contextual information, which could so far not explicitly be tied to the statements because of the simplicity of RDF. In this paper we describe a practical solution to this problem, which has been implemented in the VIKEF project.

Abstract. Knowledge Representation in the Semantic Web is mainly characterized by the capabilities of the languages OWL and RDF. When attacking non-trivial problems such as the creation and maintenance of large-scale knowledge bases in RDF, it becomes evident that RDF lacks a central feature, namely the capability of restricting the truth value of a statement to a context, with far-reaching consequences which we will hint at in this paper. We propose a solution approach to this problem: the extension of RDF knowledge bases with context features. We will present theoretical background, envisioned experiments as well as a comparative study based on two technically di erent approaches, and illustrate the applications and bene ts of contextual Knowledge Representation in the Semantic Web.

context and syllogisms from ontologies for handling discrepancies in learning resources

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