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Semantic nets as paradigms for both causal and judgemental knowledge representation

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3 Author(s)
J. R. Burns ; Coll. of Bus. Adm., Texas Tech. Univ., Lubbock, TX, USA ; W. H. Winstead ; D. A. Haworth

The use of semantic nets to represent causation in static and dynamic processes is proposed. Their conventional usage as mechanisms for representing judgemental and experimental 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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IEEE Transactions on Systems, Man, and Cybernetics  (Volume:19 ,  Issue: 1 )