Feature Article: Data Mining: An AI Perspective 25 1. Knowledge representation. Data mining seeks to discover interesting patterns from large volumes of. Using Ontologies for Knowledge Management: An Information Systems Perspective Igor Jurisica, John Mylopoulos, Eric Yu University of Toronto, Toronto, Ontario, Canada Abstract Knowledge management research focuses on the. A Structure underlying many Knowledge Representation Formalisms. This discovery leads to a new perspective on many knowledge representation formalisms. Logic & Knowledge representation Perspective: We have been studying various forms of search: Knowledge Representation - International Encyclopedia of the Social & Behavioral Sciences. In artificial intelligence, knowledge representation is the study of how the beliefs, intentions, and value judgments of an intelligent agent can be expressed in a transparent, symbolic notation suitable for automated reasoning. From a purely computational point of view, the major objectives to be achieved are breadth of scope, expressivity, precision, support of efficient inference, learnability, robustness, and ease of construction. The knowledge representation enterprise is related to the study of semantics in linguistics, the development of logical theories in analytic philosophy, and the study of mental representations in psychology; however, its focus, objectives, and techniques are quite different from any of these. A number of general architectures for knowledge representation are described, including first- order logic, other formal logics, semantic networks, and frame- based systems. The issues involved in formulating the content of a KR theory are illustrated through a sketch of representations of temporal knowledge. Finally, the article discusses two alternative to the symbolic representations of knowledge: neural networks and statistical analysis of large data corpora. A Knowledge Representation Perspective on Geometric Modeling. Geometric modeling is a very complicated matter in its own. But in many applications the real challenge is the integration of geometry with other kinds of information. That is especially true for geometric modeling and knowledge representation (KR). Feature concepts have been developed (at least in part) for this integration, but they are strongly related to current geometric modeling approaches. A deep, conceptual integration of knowledge representation and geometric modeling seems to be an open issue.
A New Perspective on Knowledge Representation, Neurodynamics, Distributed Cognition, and the Nature.
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