In the context of digital transformation of organizations and the growing volume of data, there is a demand for more transparent and explainable approaches to employee evaluation. The purpose of the study is to design and validate an ontological model (OWL 2/SHACL) that integrates employees’ cognitive indicators and sociological characteristics into a unified knowledge space to support HR processes. The scientific novelty of the work lies in the development of a unified semantic model linking data from cognitive tests, questionnaires, work context, and performance indicators; in the formulation of competency questions (CQ) that trigger reasoning mechanisms within the knowledge graph; and in the creation of patterns for predicting competency gaps, identifying the risk of overload/burnout, while ensuring ethics and non-discrimination control. The proposed approach is based on ontology engineering methodologies – METHONTOLOGY and NeOn, semantic web concepts, and psychometric methods.

This article considers the problem of several traveling salesmen. The task is to find a set of a predetermined number of disjoint cycles on a graph with weighted arcs, in which the weight (the sum of the weights of the arcs) of the largest cycle is minimal. An accurate algorithm for solving the problem based on the method of branches and boundaries has been developed. The constructed algorithm, as well as the well-known Balas' and Christofides' algorithm for solving the traveling salesman problem, uses the Hungarian algorithm for solving the assignment problem. Numerical experiments with large-dimensional random graphs have been carried out.

Several combinatorial exercises have been considered, which artificial intelligence solves with errors. The representatives of artificial intelligence examined are ChatGPT and DeepSeek systems. Questions (prompts) to these systems are provided, and the obtained answers are analyzed. Hypotheses are proposed regarding the reasons for the errors made by artificial intelligence when solving the tasks under consideration. It is suggested that similar errors may occur when using artificial intelligence for software development and other applications. Topics for further research are proposed, which may be of interest for determining the conditions for the continued use of artificial intelligence.