The basic principle of the Semantic Web carried by the RDF data model is that many RDF statements coexist all together and are universally true. However, some case studies imply contextual relevancy and truth- this is well known in the Conceptual Graph community and handled through the notion of contexts. In this paper, we present an approach and a tool for semantic annotation of textual data using graph contexts. We rely on both Natural Language Processing and Semantic Web technologies and propose a model of RDF contexts inspired by the nested Conceptual Graphs. Sentences are primarily analysed and their grammatical constituents (subject,verb,object) are extracted and mapped to RDF triples. Links between these triples are then established within a semantic scope (i.e., context). The context definition allows us to validate the generated annotations by disambiguating the misleading RDF triples. We show how far our approach is applicable to texts in Engineering Design.

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.