Semantic Nets as Paradigms for Both Causal and Judgmental Knowledge Representation

James Burns, Dwight Hayworth, Wayland Winstead

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)58-68
JournalIEEE Transactions on Systems, Man and Cybernetics
StatePublished - Jan 1989

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