Advances in Artificial Intelligence

The objective of this paper is to develop a first order logic of contexts. Dealing with contexts in an explicit way has been initially proposed by J. McCarthy [16] as a means for handling generality in knowledge representation. For instance, knowledge may be distributed among multiple knowledge bases where each base represents a specific domain with its own vocabulary. To overcome this problem, contextual logics aim at defining mechanisms for explicitly stating the assumptions (i.e. the context) underlying a theory and also mechanisms for linking different contexts, such as lifting axioms for connecting one context to another one. However, integrating knowledge supposes the definition of inter-contextual links, based not only on relationships between contextual assertions, but also on relationships built upon contexts. In this paper, we introduce a quantificational modal-based logic of contexts where contexts are represented as explicit terms and may be quantified: we show how this framework is useful for defining first order properties over