| |  | Wang, X. H. | Ontology based context modeling and reasoning using OWL read moreAbstract: Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources. | 2004 |
| | | Bouquet, P. | C-OWL: Contextualizing ontologies read moreAbstract: Ontologics are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologics can be contcxtualizcd, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contcxtualizcd or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other | 2003 |
| | | Davis, Randall | What is a Knowledge Representation read moreAbstract: Although knowledge representation is one of the
central and, in some ways, most familiar concepts
in AI, the most fundamental question about
it—What is it?—has rarely been answered directly.
Numerous papers have lobbied for one or
another variety of representation, other papers
have argued for various properties a representation
should have, and still others have focused
on properties that are important to the notion of
representation in general.
In this article, we go back to basics to address
the question directly. We believe that the answer
can best be understood in terms of five important
and distinctly different roles that a representation
plays, each of which places different and, at
times, conflicting demands on the properties a
representation should have. We argue that keeping
in mind all five of these roles provides a usefully
broad perspective that sheds light on some
long-standing disputes and can invigorate both
research and practice in the field. | 1993 |
| | | Johnson, Peter | Task-related knowledge structures: analysis, modelling and application read moreAbstract: A theoretical and methodological approach to task
modelling is described, with a worked example of
the resultant model. The theory holds that task
knowledge is represented in a person's memory
and that this knowledge can be described by a
Task Knowledge Structure (TKS). The method of
analysis has been developed for carrying out
analyses of real world tasks. The method uses a
variety of techniques for collecting information
about task knowledge. A second perspective of the
paper shows how a developed TKS model can be
decomposed into a design for a software system
to support the identified tasks within the domain
of the analysis. This decompositional method uses
the structure of frames to provide consistency
between different levels of design decomposition. | 1988 |
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