Information system for ontological modelling the subject areas

Objectives. The creation of ontologies of subject areas is considered. The goal is to develop a mathematical model of ontology and the information system for ontological modeling. The task is to reduce the complexity of ontological modeling.

Methods. As research methods, the theory of hybrid intelligence systems, the theory of sets, elements of mathematical logic, methods for developing the information systems, comparative analysis of information systems, informal analysis of information system were used.

Results. Mathematical model of ontology using the concept of metaobject is developed. Ontological modelling based on this model involves specification, conceptualization and formalization. A glossary of terms is being built at the specification stage. At the conceptualization stage, objects in the subject area and their hierarchy are defined, and then connections between objects are identified. In the formalization stage, the metaobjects and the relationships between metaobjects that correspond to objects and the relationships between objects were defined. This is considered as the ontology of the subject area. During the actualization stage, the parameters of subject area objects and their values, classes, subclasses, and instances of classes were defined. Parameters, parameter values, classes, subclasses, and instances of classes are implemented in ontology as metaobjects of relative types. An information system with a unique architecture has been developed, namely a hybrid intelligence system for the automation of ontological modelling.

Conclusion. The article conducts a comparative analysis of the developed information system with the systems used today for creation of ontologies. The analysis showed that the information system developed by paper author in most parameters is not inferior to considered systems and at the same time easier to use and expand. The mathematical model of ontology and the information system for ontological modeling of subject areas, developed by author, are tested in practical creation of ontology on ecology. On the basis of the conducted comparative analysis and informal analysis of practical use, it is concluded that ontological modeling with the help of the information system developed by author reduces the labor intensity and decreases the time of ontologies creation.

1. Greger S. E., Porshnev S. V. Construction of ontology of information system architecture. Fundamental'nye issledovanija [Fundamental Research], 2013, no. 10, рр. 2405–2409 (In Russ.).

2. Kumar A., Smith B. The Ontology of Blood Pressure: A Case Study in Creating Ontological Partitions in Biomedicine, 2015. Available at: http://ontology.buffalo.edu/medo/BPO.pdf (accessed 14.01.2021).

3. Palchunov D. E., Yakhyaeva G. E., Yasinskaya O. V. Application of theoretical and model methods and ontological modeling for automation of disease diagnosis. Vestnik Novosibirskogo gosudarstvennogo universiteta. Serija "Informacionnye tehnologii" [Bulletin of the Novosibirsk State University. Series "Information Technology"], 2015, vol. 13(3), рр. 42–51 (In Russ.).

4. Gribova V. V., Petryaeva M. V., Okun D. B., Shalfeeva E. A. Ontology of medical diagnostics for intelligent decision support systems. Ontologija proektirovanija [Design Ontology], 2018, vol. 8, no. 1(27), рр. 58–73 (In Russ.).

5. Lande D. V., Snarsky A. A. Approach to the creation of terminological ontologies. Ontologija proektirovanija [Design Ontology], 2014, no. 2(12), рр. 83–91 (In Russ.).

6. Sowa J. Building, Sharing and Merging Ontologies, 2015. Available at: http://www.jfsowa.com/ontology/ontoshar.htm (accessed 14.01.2021).

7. Samoilov D. E., Semenova V. A., Smirnov S. V. Analysis of incomplete data in the problems of building formal ontologies. Ontologija proektirovanija [Design Ontology], 2016, vol. 6, no. 3(21), рр. 317–339. (In Russ.).http://doi.org/10.18287/2223-9537-2016-6-3-317-339

8. Bukharov M. N. Sistemy gibridnogo intellekta. Hybrid Intelligence Systems. Moscow, Nauchtehlitizdat, 2005, 352 p. (In Russ.).

9. Bukharov M. N. Teorija sistem gibridnogo intellekta. Proektirovanie, standartizacija, modelirovanie i optimizacija. Theory of Hybrid Intelligence Systems. Design, Standardization, Modeling and Optimization. Moscow, Gosudarstvennoe obrazovatel'noe uchrezhdenie vysshego professional'nogo obrazovanija "Moskovskij gosudarstvennyj universitet lesa", 2008, 214 p. (In Russ.).

10. Bukharov M. N. Management of Complex Systems Based on Hybrid Intelligence. Spectehnika i svjaz' [Special Machinery and Communications], 2015, no. 3, рр. 43–55 (In Russ.).

11. Bukharov M. N. Management of Complex Scientific and Technical Systems Based on Hybrid Intelligence. Informacionno-tehnologicheskij vestnik [Information and Technological Bulletin], 2015, no. 4, рр. 72–98 (In Russ.).

12. Bukharov M. N. Tools for Creating Hybrid Intelligence Systems. Vestnik Rossijskogo novogo universiteta. Serija "Slozhnye sistemy: modeli, analiz i upravlenie" [Bulletin of the Russian New University. Series "Complex Systems: Models, Analysis and Control"], 2018, no. 1, рр. 98–105 (In Russ.).

13. Gruber T. R. A Translation Approach to Portable Ontology Specification. Knowledge Acquisition, 1993, vol. 5, iss. 2, рр. 199–220.