Semantic Nets as Paradigms for Both Causal and Judgmental Knowledge Representation

James R. Burns; Wayland H. Winstead; Dwight A. Haworth

doi:10.1109/21.24531

Semantic Nets as Paradigms for Both Causal and Judgmental Knowledge Representation

James R. Burns, Wayland H. Winstead, Dwight A. Haworth

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Abstract

The use of semantic nets to represent causation in static and dynamic processes is proposed. Their conventional usage as mechanisms for representing judgmental and experiential knowledge is reviewed. A specific semantic net called an M-labeled digraph is investigated with respect to its potential for evolving a more unified and holistic knowledge representation paradigm. A breadth-first inference engine utilizing Boolean multiplication of binary matrices is presented. Limitations of the method are discussed.

Original language	English
Pages (from-to)	58-67
Number of pages	10
Journal	IEEE Transactions on Systems, Man and Cybernetics
Volume	19
Issue number	1
DOIs	https://doi.org/10.1109/21.24531
State	Published - 1989

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10.1109/21.24531

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title = "Semantic Nets as Paradigms for Both Causal and Judgmental Knowledge Representation",

abstract = "The use of semantic nets to represent causation in static and dynamic processes is proposed. Their conventional usage as mechanisms for representing judgmental and experiential knowledge is reviewed. A specific semantic net called an M-labeled digraph is investigated with respect to its potential for evolving a more unified and holistic knowledge representation paradigm. A breadth-first inference engine utilizing Boolean multiplication of binary matrices is presented. Limitations of the method are discussed.",

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AB - The use of semantic nets to represent causation in static and dynamic processes is proposed. Their conventional usage as mechanisms for representing judgmental and experiential knowledge is reviewed. A specific semantic net called an M-labeled digraph is investigated with respect to its potential for evolving a more unified and holistic knowledge representation paradigm. A breadth-first inference engine utilizing Boolean multiplication of binary matrices is presented. Limitations of the method are discussed.

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JO - IEEE Transactions on Systems, Man and Cybernetics

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Semantic Nets as Paradigms for Both Causal and Judgmental Knowledge Representation

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