Varieties of contexts

Abstract

We believe that a deeper understanding of the uses of contexts, in terms of its impact on knowledge representation structures, as reflected by a corpus of examples, is vital to the programme of formalizing contexts in Artificial Intelligence. In this paper, we examine a number of examples from the literature from the perspective of identifying general usage patterns. We identify four important varieties of contexts -- Projection Contexts, Approximation Contexts, Ambiguity Contexts and Mental State Contexts. We define each type, describe sub-types, list benchmark examples of each sub-type, discuss their practical uses and the requirements they make of the underlying logic. We pay particular attention to the problem of lifting, i.e., of using information obtained from one context in another and describe how these different varieties of contexts tend to require different kinds of lifting rules.