With the advances in telecommunications, and the introduction of the Internet, information systems achieved physical connectivity, but have yet to establish logical connectivity. Lack of logical connectivity is often inviting disaster as in the case of Mars Orbiter, which was lost because one team used metric units, the other English while exchanging a critical maneuver data. In this Thesis, we focus on the two intertwined sub problems of logical connectivity, namely data extraction and data interpretation in the domain of heterogeneous information systems. The first challenge, data extraction, is about making it possible to easily exchange data among semi-structured and structured information systems. We describe the design and implementation of a general purpose, regular expression based Caméléon wrapper engine with an integrated capabilities-aware planner/optimizer/executioner. The second challenge, data interpretation, deals with the existence of heterogeneous contexts, whereby each source of information and potential receiver of that information may operate with a different context, leading to large-scale semantic heterogeneity. We extend the existing formalization of the COIN framework with new logical formalisms and features to handle larger

When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference -- a simple model of the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there are (at least) two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actually applies (the `internal' approach); or (if this is feasible) one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down (or `foundationalist') approach where one tries to lay down general, a priori principles and a bottom-up (or `scientific') approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment. It ends with a plea for the cooperation of the AI and Machine Learning ...

I take issue in this talk with AI formalizations of context, primarily the formalization by McCarthy and Buvac, that regard context as an undefined primitive whose formalization can be the same in many different kinds of AI tasks. In particular, any theory of context in natural language must take the special nature of natural language into account and cannot regard context simply as an undefined primitive. I show that there is no such thing as a coherent theory of context simpliciter---context pure and simple---and that context in natural language is not the same kind of thing as context in KR. In natural language, context is constructed by the speaker and the interpreter, and both have considerable discretion in so doing. Therefore, a formalization based on pre-defined contexts and pre-defined `lifting axioms' cannot account for how context is used in real-world language.

. Strawson proposed in the early seventies an attractive threefold distinction regarding how context bears on the meaning of `what is said' when a sentence is uttered. The proposed scheme is somewhat crude and, being aware of this aspect, Strawson himself raised various points to make it more adequate. In this paper, we review the scheme of Strawson, note his concerns, and add some of our own. However, our main point is to defend the essence of Strawson's approach and to recommend it as a starting point for research into intended meaning and context. `That is not it at all, That is not what I meant, at all.' T. S. Eliot, Prufrock and Other Observations (1917) 1 Introduction The following anecdote comes from the first author [3]: Not long ago, I was visiting Boston for a small workshop on context. After a demanding morning session I got into the MIT Bookstore for a bit of shopping. Walking along the isles I noticed on a crowded shelf a sign which read: / NOAM CHOMSKY'S SECTION IS A LI...

this paper is that `indexicality' is a characteristic, privileged only to natural language