DocumentCode :
3142844
Title :
The OAR Model for Knowledge Representation
Author :
Wang, Yingxu
Author_Institution :
Dept. of Electr. & Comput. Eng., Calgary Univ., Alta.
fYear :
2006
fDate :
38838
Firstpage :
1727
Lastpage :
1730
Abstract :
The cognitive models of information representation and the mechanisms of long-term memory are fundamental research areas in cognitive informatics. This paper develops an object-attribute-relation (OAR) model for describing knowledge and information representation in the brain. According to the OAR model, the human memory and knowledge are represented by relations, i.e. synaptic connections between neurons, rather than by the neurons themselves as the traditional container metaphor described. Based on the OAR model, human knowledge can be formally described as dynamic conjunctions of the existing OAR and the newly identified or generated objects, attributes, and/or relations. The OAR model can be used to explain a wide range of cognitive mechanisms and mental processes in natural and artificial intelligences such as learning, comprehension, and reasoning
Keywords :
knowledge representation; OAR model; artificial intelligence; cognitive informatics; cognitive mechanism models; information representation; knowledge representation; long-term memory; object-attribute-relation; traditional container metaphor; Artificial intelligence; Brain modeling; Cognitive informatics; Containers; Humans; Information representation; Knowledge representation; Learning; Neurons; Software engineering; AI; Cognitive informatics; OAR; knowledge representation; logical model of memory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering, 2006. CCECE '06. Canadian Conference on
Conference_Location :
Ottawa, Ont.
Print_ISBN :
1-4244-0038-4
Electronic_ISBN :
1-4244-0038-4
Type :
conf
DOI :
10.1109/CCECE.2006.277686
Filename :
4054984
Link To Document :
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