Semantic nets as paradigms for both causal and judgemental knowledge representation
James R. Burns, Wayland H. Winstead, Dwight A. Haworth · IEEE Transactions on Systems Man and Cybernetics · 1989
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.>