Open systems are given by empirical descriptions gathered from huge amount of multimodal heterogeneous data. A system with hundreds and thousands of indicators is initially represented as a «system in data» as well as a «system in relations». Thus, the system represented in such formats can be characterized in details by its empirical, statistical, and structural portraits. Both system representations («in data» and «in relations») and empirical, statistical, and structural portraits, taken all together, form an initial empirical context of the system. The physics of systems, on the basis of empirical context, implements the process of cognition, scientific understanding, and rational explanation of ontology of open system. Correctness, fullness, and completeness of ontological knowledge are assessed as a result of exploring its axiology as well as creating resources of knowledge about the system. Information resource of knowledge describes the system as empirical fact that has gained shape. Intellectual, cognitive, and technological resources of knowledge characterize the system as a sense that has gained shape, was understood and embodied, and simultaneously, they assess the extent to which ontology of the system had been scientifically understood and rationally explained. This article is devoted to the information resource, its role and significance in the processes of cognition, understanding and explanation of ontology, is aimed to analyze value of obtained knowledge (correctness, fullness, and completeness) about the system's ontology, as well as indicates the importance of requiring fullness and representativeness for initial empirical context of the system.

Authors: T. L. Kachanova, B. F. Fomin, O. B. Fomin

Direction: Informatics, Computer Technologies And Control

Keywords: Physics of open systems, resources of system knowledge, knowledge about systems ontology, cognition of systems ontology, scientific understanding of systems ontology, rational explanation of systems ontology, value of knowledge about systems ontology

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