Development of methods for ontological binding of objects in automated systems using classifiers

E.S. Dzhumaylo, V.V. Baranyuk

Abstract


This paper analyzes knowledge representation approaches and is devoted to the problem of developing formal conceptual knowledge representation model for automated systems. A development of any automated system begins from   building its knowledge representation model. A process of building such a model is rather complicated and requires special skills and experience. Methods for simplifying knowledge representation modeling and expanding developing model are proposed. The paper considers the possibility of using presented in automated systems information resources, such as classifiers while knowledge representation model developing. Information resources are the important components of automated systems. These resources also contain a lot of useful information about the domain knowledge of an automated system, so they can be useful for building domain knowledge model. Classifiers are one of the important informational resources. They provide the uniform data representation and the qualitative information interaction between the components of the system. Classifiers contain a lot of useful information about the objects of an automated system, thus they can be used for building a knowledge representation model.


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References


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