Semantic nets as paradigms for both causal and judgemental knowledge representation

Author

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

Author_Institution

Coll. of Bus. Adm., Texas Tech. Univ., Lubbock, TX, USA

Volume

19

Issue

1

fYear

1989

Firstpage

58

Lastpage

67

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

Keywords

Boolean algebra; directed graphs; grammars; inference mechanisms; knowledge representation; Boolean multiplication; M-labeled digraph; binary matrices; breadth-first inference engine; causation; dynamic processes; experimental knowledge; judgemental knowledge; judgemental knowledge representation; semantic nets; static processes; Engines; Humans; Inference algorithms; Knowledge representation; Labeling; Logic; Operations research; Sociotechnical systems;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.24531

Filename

24531