Article

METHODOLOGICAL FOUNDATIONS OF KNOWLEDGE METROLOGY FOR MEASUREMENT INTELLECTUALIZATION

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Abstract

The functioning of modern information systems takes place in conditions of considerable uncertainty and heterogeneity of information flows, which makes it difficult to process such information: solutions are inaccurate, unstable, unfounded and not interpreted, which has been repeatedly noted in the scientific literature. This article suggests one of the methodological ways to eliminate these difficulties based on the application of the regularizing Bayesian approach (RBA) and Bayesian intelligent measurements (BIM). Information processing, both in the form of data and in the form of knowledge, is performed in BIM systems on special scales, scales with dynamic constraints that ensure the integration of numerical and linguistic information based on a modified Bayesian decision inference rule. Traceability of measurement solutions is ensured by introducing a parallel branch of metrological support for information processing. The semantic components of conjugate scales of the SDC type implement the interpretability (explainability) of measurements. The introduction of metrological justification of knowledge in technologies based on RBA provides a significant increase in the efficiency of information processing, both in artificial intelligence systems that traditionally work with knowledge, and in measurement systems for the intellectualization of measurements by ensuring the stability, interpretability and required quality of the resulting solutions. In addition, it is the metrological support of information technologies that will allow for the reliable integration of artificial intelligence and measurement technologies, which is an urgent need of modern information systems.

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