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.