The Context Interchange strategy presents a novel perspective for mediated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by a context mediator through comparison of contexts axioms corresponding to the systems engaged in data exchange. In this article, we show that queries formulated on shared views, export schema, and shared “ontologies ” can be mediated in the same way using the Context Interchange framework. The proposed framework provides a logic-based object-oriented formalism for representing and reasoning about data semantics in disparate systems, and has been validated in a prototype implementation providing mediated data access to both traditional and web-based information sources. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems—Query processing; H.2.5 [Database Management]: Heterogeneous Databases—Data translation

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.

We describe the architecture and queryanswering algorithms used in the Information Manifold, an implemented information gathering system that provides uniform ac- cess to structured information sources on the World-Wide Web. Our architecture provides an expressive language for describing information sources, which makes it easy to add new sources and to model the fine-grained distinctions between their contents. The queryanswering algorithm guarantees that the descriptions of the sources are exploited to access only sources that are relevant to a given query. Accessing only relevant sources is crucial to scale up such a system to large numbers of sources. In addition, our algorithm can exploit run-time information to further prune information sources and to reduce the cost of query planning.

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Context appears in Artificial Intelligence (AI) as a challenge for the coming years as shown by the various scientific events focusing on context held since 1995. However, context is already considered in other domains, such as Natural Language Processing, although through few aspects of context. We present in this paper a survey of the literature dealing directly and explicitly with context whatever the domain is. This permits us to have a clear view of the context in AI. One of the conclusions of this survey is to point out the existence of different types of context along knowledge representation, the mechanisms of reasoning on the knowledge, and the interaction of the computer system with humans.

. Integrating data from heterogeneous data sources is a critical problem that has received a great deal of attention in recent years. There are two competing approaches to address this problem. The traditional approach, which first appeared in Multibase and more recently in HERMES and TSIMMIS, often called global-asview, defines the global model as a view on the sources. A more recent approach, sometimes referred to as local-as-view or view rewriting, defines the sources as views on the global model. The disadvantage of the first approach is that a person must re-engineer the definitions of the global model whenever any of the sources change or when new sources are added. The view rewriting approach does not suffer from this drawback, but the problem of rewriting queries into equivalent plans using views is computationally hard and must be performed for each query at run-time. In this paper we propose a hybrid approach that amortizes the cost of query processing over all queries by pr...

Context appears in AI as a challenge for the coming years as shown by the various scientific events focusing on context held since 1995. However, context is already considered in other domains, as Natural Language, although through few aspects of context. We present in this paper a survey of the literature dealing directly and explicitly with context whatever the domain is. This permits to have a clear view on the context in AI. One of the conclusions of this survey is to point out the existence of different types of context along knowledge representation, the mechanisms of reasoning on the knowledge, and the interaction of the computer system with a human.

Ontologies represent rich semantics in a lexical way. Lexical labels are used to identify concepts and relationships, though there is no bijective mapping between them. Phenomenons such as synonyms and homonyms exemplify this, and can result in frustrating misunderstanding and ambiguity. In the elicitation and application of ontologies, the meaning of the ontological knowledge is dependent on the context. We consider the role of context in ontology elicitation by introducing context in a concept definition server for ontology representation. We also adopt other features of context found in literature, such as packaging of knowledge, aligning elements of different contexts, and reasoning about contexts. Finally, we illustrate context-driven ontology elicitation with a real world case study.

Abstract. A viable ontology engineering methodology requires supporting domain experts in gradually building and managing increasingly complex versions of ontological elements and their converging and diverging interrelationships. Contexts are necessary to formalise and reason about such a dynamic wealth of knowledge. However, context dependencies introduce many complexities. In this article, we introduce a formal framework for supporting context dependency management processes, based on the DOGMA framework and methodology for scalable ontology engineering. Key notions are a set of context dependency operators, which can be combined to manage complex context dependencies like articulation, application, specialisation, and revision dependencies. In turn, these dependencies can be used in context-driven ontology engineering processes tailored to the specific requirements of collaborative communities. This is illustrated by a real-world case of interorganisational competency ontology engineering.

Introduction The idea that knowledge can be used for finding information through the network has been developed in several directions. Most proposals use knowledge representation techniques in order to model the information stored in the network (Web) nodes, and then access the information through ad hoc procedures. On the other hand, the information that we access through the browsers is organized into structures that are coded in terms of the content of each page and of its relationship to other pages. Such structures typically guide humans in the search for a specific information. Our idea is to use this kind of structural information to build knowledge based tools for the access to the network. More specifically, we are working at a system which acquires information on a subject matter by extracting from the pages an explicit representation of the underlying structure, and use it to answer user's requests by directly pointing to the page containing the desired answer. Ther