Semantic Nets as Paradigms for Both Causal and Judgmental Knowledge Representation

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

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 languageEnglish
Pages (from-to)58-67
Number of pages10
JournalIEEE Transactions on Systems, Man and Cybernetics
Issue number1
StatePublished - 1989


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