Abstract
An approach to the formation of numerical values of metadata generated on the basis of the method of semantic decomposition of numerous indicators characterizing the research and innovation activities of the university, as well as their combination based on the method of hierarchical quasi-neural network aggregation, is considered. The developed methods are necessary for monitoring the state of scientific and innovative activities of the university, as a first step in building its management system. The aim of the work is to develop a method of hierarchical aggregation of data based on their presentation in the form of a quasi-neural network structure, the input of which is the data itself, and the output is a set of metadata or indicators of the state of the scientific and innovative activity of the university, characterizing the degree of data compliance with their planned criteria indicators. The leading approach includes: semantic decomposition of data into elementary aggregates; formation of aggregate metadata in the form of indicators characterizing the degree of compliance of aggregated data with planned criteria indicators.
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