The article presents an attempt to describe mathematically the effect of the semantic kernel of a web resource on the Internet. In accordance with the theory of marketing, the product that we want to sell on the network is characterized by the following basic properties: price, time and place. In other words, a potential buyer wants to receive a given product in the right place at a given time. To satisfy this need, it is necessary to use the classic component of marketing, product promotion. However, this component is now becoming a fully virtual instrument. This tool functions in a hypertext, video and image environment. Therefore, the user analyzes the meaning of these elements in order to get the desired product. The results of web projects carried out in this area indicate the emergence of a new phenomenon, which reflects the main meaning of virtual promotion – this is the semantic core. The core is a short annotation of the main properties of the product, its location and time of appearance. Therefore, the purpose of this article is both a presentation of a new object of research and a mathematical description. It is assumed that the semantic core is formed on the basis of natural language terms. In other words, the semantic core is a set of keywords that are grouped by meaning. We propose to use data mining approaches for clustering to group terms. The classic clustering method at the moment is k-means. The article presents a model of the semantic core based on this method. This method and its distance function are considered as the second stage of web content processing. At the first stage, web content is converted into a semantic web. However, the k-means technique has significant drawbacks when modeling the semantic core. Therefore, in the development of this idea, the work shows an alternative way to modeling the kernel. As an alternative approach, the construction of clusters based on the concept of maximum flow is considered. This approach has the significant advantage that the type of links in the semantic network overlaps with the type of distance function in this method. As a result, on a real web project, the effect of the connection between the semantic core model and the level of new users of the web resource was demonstrated over the past five years.

Keywords: semantic kernel, keyword, k-means, max flow.

Author Biographies

Sergey Orekhov, National Technical University «Kharkov Polytechnic Institute»

PhD, Associate Professor, National Technical University «Kharkov Polytechnic Institute», Accosiate Professor of Software Engineering and Management Information Technologies department; Kharkov, Ukraine; ORCID:; e-mail:

Hennadiy Malyhon, National Technical University «Kharkiv Polytechnic Institute»

Post graduate, National Technical University «Kharkov Polytechnic Institute», Post graduate of Software Engineering and Management Information Technologies department; Kharkov, Ukraine; ORCID:; e-mail:


Godlevsky M., Orekhov S., Orekhova E. Theoretical Fundamentals of Search Engine Optimization Based on Machine Learning. CEUR WS. USA, 2017. vol. 1844, pp. 23–32.

Orekhov S., Malyhon H., Stratienko N., Goncharenko T. Software Development for Semantic Kernel Forming. CEUR WS. USA, 2021. vol. 2870. pp. 1312–1322.

Amit K. Artificial Intelligence and Soft Computing. Behavioral and Cognitive Modeling of the Human Brain. USA: CRC Press LLC Publ., 2000. 788 p.

Konnov I. V., Kashina O. A., Gilmanova E. I. Reshenie zadachi klasterizazii metodamai optmizazii na grafah [Solution of clusterization problem by graph optimization methods]. Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki [Scientific bulletin of Kazan University. Physical and Mathematical Series]. Kazan, Kazan University Publ., 2019, issue 161, no. 3, pp. 423–437.

Cherenkov I, Orekhov S. Approach for extracting events from news stream. Eastern–European Journal of Enterprise Technologies. 2013. vol. 1, no. 4 (61), pp. 62–64.

Preisach C., Burkhardt H., Schmidt-Thieme L, Decker R. Data Analysis, Machine Learning and Applications. Germany: SpringerVerlag Publ., 2008. 703 p.

Han J., Kamber M, Pei J. Data Mining Concepts and Techniques. USA: Morgan Kaufmann Publ., 2012. 740 p.

Shumeyko А.А., Sotnik S.L. Intellektualnuy analyz dannuh. Vvedeniye v Data Mining [Intelligent data analysis. Introduction in Data Mining]. Dnepr: Belaya Publ., 2015. 223 p.

Willams U. Т., Lans D. N. Metodu ierarhicheskoy klassifikazii [Methods of hierarchical classification]. Statistical methods of computer machine. Moscow: Nauka Publ., 1986. pp. 269–301.

Venugopal K.R., Srinivasa K.G. and Patnaik L.M. Soft Computing for Data Mining Applications. Germany: Springer Publ., 2009. 354 p.

Witten Ian H., Frank E. Data Mining. Practical Machine Learning Tools and Techniques. USA: Morgan Kaufmann Publ., 2009. 558 p.

Duran B. Klasternuy analiz [Cluster analysis]. Moscow: Statistica Publ., 1977. 128 p.

Zamyatin A. V. Intelektualnuy analiz dannuh. Uchebnoe posobie [Intelligent data analysis: tutorial]. Tomsk: Tomsk state university Publ., 2016. 120 p.




How to Cite

Orekhov, S., & Malyhon, H. (2021). MODELLING SEMANTIC KERNEL OF WEB RESOURCE . Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, (1 (5), 52–55.