In this paper we present a new semantics, called Local Models Semantics, and use it to provide a foundation to reasoning with contexts. This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning: reasoning with viewpoints and reasoning about belief.

this article was going through many versions as the ideas developed, and the mutual influences cannot be specified. This work was partly supported by DARPA contract NAG2-703 and ARPA/ONR grant N00014-94-1-0775

It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature. Our intuition is that reasoning is usually performed on a subset of the global knowledge base. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McC87, McC91]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported i...

I wish honorable gentlemen would have the fairness to give the entire context of what I did say, and not pick out detached words. (Cobden, Speeches 46, 1849, quoted intheOED) The importance of contextual reasoning is emphasized by various researchers in AI.

In this paper we provide a foundation of a theory of contextual reasoning from the perspective of a theory of knowledge representation. Starting from the so-called metaphor of the box, we firstly show that the mechanisms of contextual reasoning proposed in the literature can be classified into three general forms (called localised reasoning, push and pop, and shifting). Secondly, we provide a justification of this classification, by showing that each mechanism corresponds to operating on a fundamental dimension along which context dependent representations may vary (namely, partiality, approximation, and perspective). From the previous analysis, we distill two general principles of a logic of contextual reasoning. Finally, we show that these two principles can be adequately formalised in the framework of MultiContext Systems. In the last part of the paper, we provide a practical illustration of the ideas discussed in the paper by formalising a simple scenario, called the Magi...

A central theme of the semantic web is that programs should be able to easily aggregate data from di#erent sources. Unfortunately, even if two sites provide their data using the same data model and vocabulary, subtle differences in their use of terms and in the assumptions they make pose challenges for aggregation. Experiences with the TAP project reveal some of the phenomena that pose obstacles to a simplistic model of aggregation. Similar experiences have been reported by AI projects such as Cyc, which has lead to the development and use of various context mechanisms. In this paper we report on some of the problems with aggregating independently published data and propose a context mechanism to handle some of these problems. We briefly survey the context mechanisms developed in in AI and contrast them with the requirements of a context mechanism for the semantic web. Finally, we present a context mechanism for the semantic web that is adequate to handle the aggregation tasks, yet simple from both computational and model theoretic perspectives.

It is essential to reduce the cost of integrating information sources and to provide a path that allows for incremental integration that can be responsive to users' demands. This paper presents an approach to integrating disparate heterogeneous information sources that uses context logic. Our use of context logic reduces the up-front cost of integration, provides an incremental integration path, and allows semantic conflicts within a single information sources or between information sources to be expressed and resolved. Introduction The number of online network-accessible information sources grows daily. The information promises to provide tremendous value for individuals and corporations. The promise will remain unfulfilled, however, until it is possible to integrate and assimilate information from multiple heterogeneous sources. Because it is impossible to predict the users and patterns of usage in our changing information environment, information providers are not willing to pay a ...

Temporal abstraction is the task of abstracting higher-level concepts from time-stamped data in a contextsensitive manner. We have developed and implemented a formal knowledge-based framework for decomposing and solving that task that supports acquisition, maintenance, reuse, and sharing of temporalabstraction knowledge.

In this paper we investigate the simple logical properties of contexts. We describe both the syntax and semantics of a general propositional language of context, and we give a Hilbert style proof system for this language. A propositional logic of context extends classical propositional logic in two ways. Firstly, a new modality, ist(; OE), is introduced. It is used to express that the sentence, OE, holds in the context, . Secondly, each context has its own vocabulary, i.e. a set of propositional atoms which are defined or meaningful in that context. The main results of this paper are the soundness and completeness of this Hilbert style proof system. We also provide soundness and completeness results (i.e., correspondence theory) for various extensions of the general system. Finally, we prove that our logic is decidable, and give a brief comparison of our semantics to Kripke semantics. 1 Introduction In this paper we investigate the simple logical properties of contexts. Contexts were...

We present a graph-based KR model issued from Sowa's conceptual graphs but studied and developed with a speci c approach. Formal objects are kinds of labelled graphs, which maybesimple graphs or nested graphs. The fundamental notion for doing reasonings, called projection (or subsumption), is a kind of labelled graph morphism. Thus, we propose a graphical KR model, where \graphical " is used in the sense of [Sch91], i.e. a model that \uses graph-theoretic notions in an essential and nontrivial way". Indeed, morphism, which is the fundamental notion for any structure, is at the core of our theory. We de ne two rst order logic semantics, which correspond to di erentintuitivesemantics, and proveinboth cases that projection is sound and complete with respect to deduction. This paper is almost identical to the paper ap-peared in the KR'98 proceedings. It provides mi-nor corrections. 1