Control Systems and Computers, N3, 2018, Article 2


Upr. sist. maš., 2018, Issue 3 (275), pp. 18-32.

UDC 004.94

Halyna A. Pidnebesna, junior research scientist, Departament for Technologies of Inductive Modelling, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine,


Introduction. In the field of information technology, one of the most actual problems is the development and improvement of intelligent computer systems that to a certain degree simulate the process of human reasoning. For their functioning it is necessary to formalize knowledge of experts and implement them in a form that is acceptable for computer processing. One of the promising ways of this representation is ontology. An ontological approach allows to combine in a single structure the data of various types, their properties, to define relations between them and to define the functions of their interpretation. Different level of knowledge generalization makes ontology a universal means of representing information of various levels of abstraction, from the most general concepts (ontology of higher level, metanetology) to ontologies of subject areas and applied tasks that solve specific problems.

GMDH is one of the effective methods of modeling of complex systems by statistical data. The actual task is the analysis and structuring of the subject area in order to further formalize the knowledge of the inductive modeling domain based on GMDH.

The purpose of the article. To analyze and to structure the domain of the inductive modeling based on GMDH with the aim of further formalization of domain knowledge using ontologies.

Methods. Basic definitions, characteristics and approaches are based on the review and analysis of thematic publications and the results of our own research.

Result. The analysis of the industry of inductive modeling from the point of view of ontological engineering is carried out, the principles for constructing the corresponding ontologies of various levels are defined — from the metamodel of the modeling process to applied task ontologies.

Fragments of ontologies of the main components of the inductive modeling process are given. Key parameters were determined, which made it possible to generalize and expedient the design of multifunctional software modules when developing the computer-based inductive modeling tools based on GMDH.

Conclusion. The result of the analysis and structuring of the GMDH based inductive modeling subject area allows to simplify the development of the software systems based on knowledge, to expand the possibility of modifying the existing computer simulation systems and to solve various applied problems.

Download full text! (In Ukrainian).

Keywords: ontology, inductive modeling, GMDH, metemodel.


  1. Brooks, H., 1987. “Expert Systems and Intelligent Information Retrieval”, Information Processing & Management, 23 (4), pp. 367–382.
  2. Bille, W., Pellens, B., Kleinermann, F. et al., 2004. “Intelligent Modelling of Virtual Worlds Using Domain Ontologies”, Proc. of the Workshop of Intelligent Comp. (WIC), held in conjunction with the MICAI 2004 conf., Mexico, Mexico City, pp. 272–279, ISBN 968-489-024-9.
  3. Mizoguchi, R., Bourdeau, J., 2002. “Using ontological engineering to overcome common AI-ED problems,” Journal of Artificial Intelligence and Education, 11, pp. 107–121.
  4. Berners–LeeTim, 1998. Semantic Web Road map, [online] Available at: < emantic.html> [Accessed 2 Sept. 1998].
  5. Berners–Lee,T., HENDLER, J., LASSILA, O., 2001. (May 17, 2001). The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, 284. pp. 34–43. doi:10.1038/scientificamerican0501-34, [ LBSC690/SemanticWeb.html].
  6. Gribova, V.V., Klescheev, A.C., 2012. “Ontological paradigm of programming”. Open Semantic Technologies for Intelligent Systems conference OSTIS-2012. pp. 213–220. []. (In Russian).
  7. Stepashko, V.S. “Conceptual bases of intellectual modeling”, Upravlâûŝie sistemy i mašiny, 2016, 4. pp. 3–15. (In Russian).
  8. Stepashko, V.S., Savchenko, Ye.A., Pidnebesna, H.A., 2010. “Inductive modeling as a process of sequential decision making”. Proc. the conf. “Intelligent decision making systems and artificial intelligence problems”, Yevpatoria, May 17-21, 2010, Kherson: KhNTU, T. 2. pp. 187–191. (In Ukrainian).
  9. Stepashko, V.S., 2010. “Elements of the theory of inductive modeling”. The state and prospects of the development of computer science in Ukraine: monograph. Kyiv: Naukova dumka, pp. 481–496. (In Ukrainian).
  10. Stepashko, V.S., 1988. “GMDH algorithms as the basis for automating the process of modeling on experimental data”. Automation, 4. p. 44–55. (In Russian).
  11. Self-organizing Modeling, [online] Available at: <> [Accessed 16 Dec. 2017].
  12. Forecasting Software for Your Business, [online] Available at: <> [Accessed 12 Dec. 2017].
  13. Knowledge-based_systems, [online] Available at: <> [Accessed 16 Dec. 2017].
  14. Reid, G. SMITH., 1985. Knowledge-Based Systems. Concepts, Techniques. Examples, [online] Available at: < nowledge-Based_Systems_-_Concepts_Techniques_Examples_08-May-1985.pdf> [Accessed 15 Dec. 2017].
  15. Mettrey, W., 1987. “An Assessment of Tools for Building Large Knowledge-BasedSystems”. AI Magazine. 8(4),
  16. Knowledge-based-systems, [online] Available at: <> [Accessed 17 Dec. 2017].
  17. Knowledge based systems, [online] Available at: <> [Accessed 17 Dec. 2017].
  18. Pіdnebesna, H.A., 2013. “Conceptual development of ontology for the design of inductive modeling”. Inductive modeling of complex systems. Coll. sciences works. K .: MNNTІTS, 5, pp. 248–256 (In Ukrainian).
  19. Valkman, Yu.R., 2011. Ontologies: formal and informal. Report at the seminar “Pattern computer”, 08.11.2011, [online] Available at: <> [Accessed 18 Dec. 2017] (In Russian).
  20. Gruber, T.R., 1993. “A translation approach to portable ontologies”. Knowledge Acquisition, 5(2), pp. 199–220.
  21. Kryvyy, S.L., 2016. “Formalized ontological models in scientific research”. Upravlâûŝie sistemy i mašiny, 3, pp. 4–15 (In Russian).
  22. Gavrilova, T.A., Khoroshevsky, V.F., 2000. Knowledge bases in intellectual systems. SPb, 384 p. (In Russian).
  23. Guarino, N. Understanding, Building, and Using Ontologies, [online] Available at: < AW/KAW96/ uarino / guarino.html> [Accessed 7 Oct. 2017].
  24. Skobelev, P.O., 2012. “Ontologies of activity for situational management of enterprises in real time”. Design Ontology. Samara: New technology, 1, pp. 6–39. (In Russian).
  25. Pospelov, D.A.,1989. “Intellectual interfaces for computers of new generations”. Electronic computing, Zbornik statey, M .: Radio and communication, 3, p. 4–20. (In Russian).
  26. Pіdnebesna, H.A., 2014. “Ontologic training to the design of the interface in the inductive mode systems”. Inductive modeling of complex systems. Zbіrnyk nauk. prats. K .: MNNTІTS, 6. pp. 117–126.
  27. Pіdnebesna, H.A., 2017. “Ontologies and values for the development of such information technologies”. Inductive modeling of complex systems. Zbіrnyk nauk. prats. K .: MNNTІTS, 9. pp. 174–187.
  28. Velichko V., Malakhov K., Semenkov V., Strizhak A., 2014. Complex ontology engineering tools, [online] Available at: <> [Accessed 16 Oct. 2017].
  29. Palagin, A.V., Kryvyi, S.L., Petrenko, N.G., 2012. “Ontological methods and tools for processing subject knowledge”. Lugansk: publishing of Dahl, 323 p.
  30. OWL 2 Web Ontology Language Document Overview (Second Edition), [online] Available at: < owl2-overview/> [Accessed 10 Oct. 2017].
  31. RDF Vocabulary Description Language 1.0: RDF Schema (RDFS), [online] Available at: < sw/wiki/RDFS> [Accessed 10 Oct. 2017].
  32. Knowledge interchange format, [online] Available at: < Format> [Accessed 10 Oct. 2017].
  33. Ding, L., Kolari, P., Ding, Z., AVANCHA, S, Finin, T, Joshi, A, 2005. Using Ontologies in the Semantic Web: A Survey, [online] Available at: <> [Accessed 20 Oct. 2017].
  34. Protege – Stanford University, [online] Available at: <> [Accessed 20 Oct. 2017].
  35. Open Knowledge Base Connectivity Home Page, [online] Available at: <> [Accessed 20 Oct. 2017].
  36. Stepashko, V.S., 1979. “Optimization and generalization of schemes for sorting models in algorithms of GMDH. Automation, 4. pp. 36–43 (In Russian).
  37. Valkman Yu.R., Stepashko P.V., 2015. “On the way of building ontology of intellectual modeling”. Inductive modeling of complex systems. Coll. sciences works. K .: MNNTІTS,7, pp. 101–115 (In Russian).
  38. Pidnebesna, H.A., Stepashko, P.V., 2018. “Ontological characteristics of the process of intellectual modeling”. ISDMCI’2018, Zaliznyi Port. Kherson: KhNTU Publishing House, pp. 272-274. (In Ukrainian).
  39. Stepashko, V.S., 1991. “On the task of structuring knowledge of an expert in the field of modeling by empirical data”. Kibernetika i vycislitelnaa tehnika, 92, pp. 80–83. (In Russian).
  40. Stepashko, V.S., Yefimenko, S.M., Savchenko, Ye.A., 2014. Computer experiment in inductive modeling. Kyiv: Naukova Dumka, 222 p. (In Ukrainian).
  41. Stepashko, V.S., 1981. “Combinatorial algorithm GMDH with the optimal scheme of sorting models”. Automation, 3, pp. 31–36. (In Russian).
  42. Stepashko, V.S., 1983. “The final selection procedure for reducing the total enumeration of models”. Automation, 4, pp. 84–88. (In Russian).
  43. Stepashko, V.S., Bulgakova, O.S., Zosimov, V.V., 2018. “Interactive algorithms for inductive modeluvania”. K .: Naukova Dumka, 190 p. (In Ukrainian).
  44. Moroz, O.H., Stepashko, V.S., 2016. “Porous analogous generators of model structures for the intergenerational algorithms of GMDH”. Inductive modeling of complex systems. Coll. sciences works. K .: MNNTІTS, 8, pp. 117–126. (In Ukrainian).
  45. Stepashko, V.S., Kocherga, Yu.L., 1985. “Methods and criteria for solving problems of structural identification”. Automation, 5, pp. 29–37.
  46. Stepashko, V., Bulgakova, O., Zosimov, V., 2017. “Construction and Research of the Generalized Iterative GMDH Algorithm with Active Neurons”. Advances in Intelligent Systems and Computing II, v. 689, pp. 492–510.
  47. Moroz, O., Stepashko, V., 2017. “Hybrid sorting-out algorithm COMBI-GA with evolutionary growth of model complexity”. Advances in Intelligent Systems and Computing II, v. 689, pp. 346–360. 978-3-319-70581-1_25.
  48. Stepashko, V., Yefimenko, S., 2008. “Paralleling for Solving of Modelling Problems”. Proceedings of the II International Conference on Inductive Modelling ICIM–2008, 15–19 September 2008, Kyiv, Ukraine. Kyiv: IRTC ITS NANU, pp. 172–175.
  49. Stepashko, V.S., Yefimenko, S.N., “Sequential estimation of parameters of regression models”. Kibernetika i sistemny analiz. 2005, 4, pp. 184–187. (In Russian).
  50. Ivakhnenko,A.G., Savchenko,E.A., 2008. “Investigation of efficiency of additional determination method of the model selection in the modeling problems by application of GMDH algorithm”. J. Autom. Inf. Sci. 40(3), 2, pp. 47–58.
  51. Stepashko, V.S., Pіdnebesna, G.A., 2011. “Concepts of the flexible multifunctions of the modular modules are the basis of the design of inductive modeling”. Inductive modeling of complex systems. Coll. sciences works. K .: MNNTІTS, 3, pp. 216–223. (In Ukrainian).
  52. Pidnebesna, H., 2017. “On Constructing Ontology of the GMDH-based Inductive Modeling Domain”. Proceedings of the XII IEEE International Conference CSIT–2017 & International Workshop on Inductive Modeling, September 05–08, 2017, Lviv, Ukraine. Lviv: Publisher “Vezha&Co”, pp. 511–513.
  53. Pidnebesna, H., Stepashko, V., 2018. “On Construction of Inductive Modeling Ontology as a Metamodel of the Subject Field”. Proceedings of the International Conference “Advanced Computer Information Technologies” ACIT–2018, Ceske Budejovice, Czech Republic, June 1–3, pp. 137–140. ISBN 978-966-654-489-9.

Received 05.09.2018