Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Artificial intelligence and mathematical theory of computation
Representing multiple theories
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Quantificational logic of context
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Logical sentences as the intent of concepts
Journal of Computer Science and Technology
Foundations of validating reusable behavioral models in engineering design problems
WSC '04 Proceedings of the 36th conference on Winter simulation
A data-oriented survey of context models
ACM SIGMOD Record
A compositional framework for the specification of interaction protocols in multiagent organizations
Web Intelligence and Agent Systems
A Conceptual Modeling Framework for Expressing Observational Data Semantics
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Semantics in Data and Knowledge Bases
Semantic and Conceptual Context-Aware Information Retrieval
Advanced Internet Based Systems and Applications
Context dependency management in ontology engineering: a formal approach
Journal on data semantics VIII
Provenance context entity (PaCE): scalable provenance tracking for scientific RDF data
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Scalable interoperability through the use of COIN lightweight ontology
ODBIS'05/06 Proceedings of the First and Second VLDB conference on Ontologies-based databases and information systems
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
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We believe that a deeper understandingof 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 formalizingcon texts 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 underlyinglog ic. 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 liftingrules.