You are in:Home/Publications/ElBedwehy MN, Ghoneim ME, Hassanien AE, Azar AT (2014). A Computational Knowledge Representation Model for Cognitive Computers. Neural Computing and Applications, Springer. DOI 10.1007/s00521-014-1614-0 [ISI Indexed: Impact Factor: 2.505].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
ElBedwehy MN, Ghoneim ME, Hassanien AE, Azar AT (2014). A Computational Knowledge Representation Model for Cognitive Computers. Neural Computing and Applications, Springer. DOI 10.1007/s00521-014-1614-0 [ISI Indexed: Impact Factor: 2.505].
Authors: Mona Nagy ElBedwehy, MohamedElsayed Ghoneim, Aboul Ella Hassanien& Ahmad Taher Azar
Year: 2014
Keywords: Not Available
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Local/International: International
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Abstract:

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.

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