• 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