Research Article Ontology of Mathematical Modeling Based on Interval Data Mykola Dyvak , 1 Andriy Melnyk , 1 Artur Rot , 2 Marcin Hernes , 2 and Andriy Pukas 1 1 Department of Computer Science, West Ukrainian National University, 11 Lvivs’ka Str., Ternopil 46000, Ukraine 2 Faculty of Management, Wroclaw University of Economics and Business, Komandorska 118/120, Wroclaw 53-345, Poland Correspondence should be addressed to Andriy Melnyk; melnyk.andriy@gmail.com Received 8 February 2022; Revised 9 April 2022; Accepted 13 June 2022; Published 19 July 2022 Academic Editor: Andrea Murari Copyright©2022MykolaDyvaketal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An ontological approach as a tool for managing the processes of constructing mathematical models based on interval data and further use of these models for solving applied problems is proposed in this article. Mathematical models built using interval data analysis are quite effective in many applications, as they have “guaranteed” predictive properties, which are determined by the accuracy of experimental data. However, the application of mathematical modeling methods is complicated by the lack of software tools for the implementation of procedures for constructing this type of mathematical models, creating an ontological model that operates by the categories of the subject area of mathematical modeling, regardless of the modeling object proposed in this article. is approach has made it possible to generate tools for mathematical modeling of various objects based on the interval data analysis for any software development environment selected by the user. e technology of creating the software on the basis of the developed ontological superstructure for mathematical modeling using the interval data for different objects, as well as various forms of user interface implementation, is presented in this article. A number of schemes, which illustrate the technology of using the ontological approach of mathematical modeling based on interval data, are presented, and the features of its interpretation when solving environmental monitoring problems are described. 1. Introduction Mathematical modeling is one of the main tools that allows describing the object in a simple form, exploring it, and predicting behavior. Mathematical modeling is understood as the process of building a model and its application to certain applied problems [1–4]. Mathematical modeling processes consist of a large number of procedures, which are mainly implemented in the relevant tools, that is, in the form of certain software systems [3, 4]. Examples of these software environments are Matlab, GNU Octave, Scilab, and SageMath. ese tools are mul- tipurpose and well developed. However, practitioners often need to use more specialized tools for building mathematical models, as well as to adapt existing tools to nonstandard conditions that are absent in the noted environments. In this case, there are difficulties in using and interpreting such tools because the simulation procedures are hidden from the researcher, and this makes it difficult to use them by making appropriate software changes [4–8]. In this case, the most appropriate solution is to create an ontological description of certain methods of mathematical modeling. It describes in detail the components of a model building process and its application. en this ontological description is used to generate appropriate software. is approach, on the one hand, will allow the integration of the created software in various applied systems and, on the other hand, will make changes to existing software [4, 9–12]. e availability of ontological descriptions of modeling processes based on certain methods makes it possible to unify the software used for a wide range of tasks. It enables, based on experience, a repository of mathematical model creation that can be used to model a wide range of math- ematically similar properties [13–23]. e positive effect of this approach will be a significant simplification of the process of creating tools for both the modeling processes organization and their application to applied problems. One of the directions of mathematical modeling is the inductive approach, which is based on a self-organized process Hindawi Complexity Volume 2022, Article ID 8062969, 19 pages https://doi.org/10.1155/2022/8062969