Abstract
Creation of digital twins of various objects and processes relevant to the enterprise activity for management tasks is becoming increasingly relevant in the era of digital transformation of enterprises, which is part of the fourth industrial revolution. It can be both individual products, production facilities and processes, or entire enterprises. The creation of the last type of digital twins, i.e. a digital twin of organizations (DTO), is the least developed topic. This is largely due to the low degree of formalization of many aspects relevant to enterprises, as well as the presence of a significant gap between the physical and information worlds in the case of an insufficient degree of the organization digitalization. The main topic of this research is a conception of methodology for DTO creation based on ontologies, knowledge graphs and enterprise architecture. Enterprise Architecture has generated many modeling-based methods for description and formalization of the activities and structure of enterprises in terms of business – and IT-architecture, but by nature, most models are qualitative and static and thus not applicable for the task of digital twin creation. Presentation of various architectural models in the form of semantic networks for structuring the subject domain, as well as using as a data structure for graph databases, allows developing a methodology for building digital twins of organizations. Usage of the methodology can provide enterprise data integration, continuous data acquisition, data warehousing and prepare data for further analysis to support managerial decision-making.
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The reported study was funded by the Russian Foundation for Basic Research (RFBR), project number 20-07-00854.
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Kubelskiy, M. (2021). Application of Semantic Networks and Enterprise Architecture Approaches for Creation of Digital Twins of Organizations. In: Antonyuk, A., Basov, N. (eds) Networks in the Global World V. NetGloW 2020. Lecture Notes in Networks and Systems, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-030-64877-0_10
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