Semantic Nets as Paradigms for Both Causal and Judgmental Knowledge Representation

James Burns, Dwight Hayworth, Wayland Winstead

Research output: Contribution to journalArticle

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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