Organization of information support for business processes at aviation enterprises by means of ontological engineering

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.126673

Keywords:

aviation enterprises, language of operating semantics, decision support system, ontological engineering

Abstract

We propose using the deductive principle of inference, which takes into consideration child relations between concepts of a subject domain in the process of forming a reasoning chain and, thus, ensures correctness of knowledge, contained in an ontological system. In this case, an ontological system is directly the intelligence core of a decision support system for organization of business processes at an aviation enterprise. For implementation of the declared principle, three methods of knowledge manipulation in the environment of an ontological system were proposed: bottom-up, top-down and combined, which implies the alternating use of the first two methods. Application of the combined method gives the possibility to eliminate knowledge incompleteness and inconsistency. Formalization of inference process on the knowledge in the environment of an ontological system with the use of the proposed methods is based on a description of the internal relations between concepts, integrating a set of concepts and fields with the help of the language of operational semantics, as well as on the introduction of external relations that characterize structural relations of concepts, including hierarchical relations of aggregation and synthesis.

The possibility of re-using, that is the multiple use of ontological information structures in making decisions on organization of business processes at aviation enterprises will make it possible to enhance efficiency of production decisions and their operative making.

The obtained results create the methodological basis for the development of software of inference organization on knowledge directly in the environment of ontologies, which is proposed to use as part of the core of production DSS.

Author Biographies

Igor Shostak, N. E. Zhukovsky National Aerospace University "Kharkiv Aviation Institute" Chkalova str., 17, Kharkiv, Ukraine, 61070

Doctor of Technical Sciences, Professor

Department of Software Engineering

Mariia Danova, N. E. Zhukovsky National Aerospace University "Kharkiv Aviation Institute" Chkalova str., 17, Kharkiv, Ukraine, 61070

PhD

Department of Software Engineering

Yuri Romanenkov, N. E. Zhukovsky National Aerospace University "Kharkiv Aviation Institute" Chkalova str., 17, Kharkiv, Ukraine, 61070

Doctor of Technical Sciences, Associate Professor

Department of Management

Oleg Bugaienko, N. E. Zhukovsky National Aerospace University "Kharkiv Aviation Institute" Chkalova str., 17, Kharkiv, Ukraine, 61070

PhD

Department of Chemistry, Ecology and Expert Technologies

Maksym Volk, Kharkiv National University of Radio Electronics Nauki ave., 14, Kharkiv, Ukraine, 61122

PhD, Associate Professor

Electronic Computers Department

Marina Karminska-Bielobrova, National Technical University "Kharkiv Polytechnic Institute" Kyrpychova str., 2, Kharkiv, Ukraine, 61002

PhD

Department of production organization and personnel management

References

  1. Melihov, A. N., Bernshteyn, L. S., Korovin, S. Ya. (1990). Situacionnye sovetuyushchie sistemy s nechetkoy logikoy. Moscow: Nauka, 271.
  2. Aliev, R. A., Abdikeev, N. M., Shahnazarov, M. M. (1990). Proizvodstvennye sistemy s iskusstvennym intellektom. Moscow: Radio i svyaz', 262.
  3. Aliev, R. A., Mamedova, G. A. (1993). Identifikaciya i optimal'noe upravlenie nechetkimi dinamicheskimi sistemami. Izv. AN. Seriya: Tekhnicheskaya kibernetika, 6, 1–9.
  4. Anisimov, V. Yu., Borisov, E. V. (1991). Metody dostovernosti realizacii nechetkih otnosheniy v prikladnyh sistemah iskusstvennogo intellekta. Izv. AN. Seriya: Tekhnicheskaya kibernetika, 5, 24–89.
  5. Gorban', A. N., Rossiev, D. A. (1996). Neyronnye seti na personal'nom komp'yutere. Novosibirsk: Nauka, 276.
  6. Aliev, R. A., Cerkovniy, A. E., Mamedova, G. A. (1991). Upravlenie proizvodstvom pri nechetkoy iskhodnoy informacii. Moscow: Energoatomizdat, 201.
  7. Bershteyn, L. S., Kazupeev, V. M., Korovin, S. Ya., Melihov, A. I. (1990). Parallel'niy processor nechetkogo vyvoda dlya situacionnyh ekspertnyh system. Izv. AN. Seriya: Tekhnicheskaya kibernetika, 5, 86–90
  8. Logicheskiy podhod k iskusstvennomu intellektu: Ot modal'noy logiki k logike baz dannyh (1998). Moscow: Mir, 494.
  9. Mertins, K., Jardim-Gonçalves, R., Popplewell, K., Mendonça, J. P. (Eds.). (2016). Enterprise Interoperability VII: Enterprise Interoperability in the Digitized and Networked Factory of the Future. Springer, 344. doi: 10.1007/978-3-319-30957-6
  10. Shostak, I., Sobchak, A., Firsova, H., Kushnarenko, O. (2016). Ahrehatsiya danykh dlia formuvannia vyrobnychykh rishen na promyslovykh pidpryiemstvakh iz vykorystanniam ontolohichnykh system. Traiektoriya nauky, 3 (8).
  11. Ada, Ş., Ghaffarzadeh, M. (2015). Decision making based on management information system and decision support system. International Journal of Economics, Commerce and Management, III (4), 1–14.
  12. Kruglov, V. V. (2002). Iskusstvennye neyronnye seti: Teoriya i praktika. Moscow: Goryachaya liniya-Telekom, 382.
  13. Danova, M. A., Shostak, I. V. (2012). Ontologicheskiy podhod k kompleksnoy komp'yuterizacii processa prognozirovaniya nauchno-tekhnicheskogo razvitiya regiona. Suchasni informatsiyni tekhnolohiyi v ekonomitsi ta upravlinni pidpryiemstvamy, prohramamy ta proektamy: tez. dop. X Mizhnar. nauk.-prakt. konf. Alushta, 60–61.
  14. Shostak, Y. V., Danova, M. A. (2017). Analiz innovatsiynoi diyalnosti rehioniv zasobamy ontolohichnoho inzhynirynhu. Informacionnye sistemy i tekhnologii: tez. dokl. 6-y mezhdunar. nauchn.-tekhn. konf. Kharkiv, Koblevo, 57–58.
  15. Kudelina, D. B, Shostak, I. V., Gruzdo, I. V. (2016). Upravlenie znaniyami razrabotchikov softvernoy firmy po sertifikacii programmnyh produktov na osnove ontologicheskogo podhoda. Systemy obrobky informatsiyi, 5 (142), 50–55.
  16. Vorob'ev, Yu. A., Nechiporuk, N. V., Kobrin, V. N., Shostak, I. V. (2014). Modeli ontologiy i ontologicheskoy sistemy podderzhki prinyatiya resheniy po vyboru ruchnyh impul'snyh ustroystv. Naukovi notatky, 46, 77–83.
  17. Shostak, I., Butenko, I. (2012). Ontology approach to realization of information technology for normative profile formimg at critical software certification. Zbirnyk naukovykh prats viyskovoho instytutu KNU im. T.H. Shevchenko, 38, 250–253.
  18. Cvetkov, V. Ya. (2017). Kognitivnoe upravlenie. Moscow: MAKS Press, 69.
  19. Katalnikova, S., Novickis, L. (2018). Choice of Knowledge Representation Model for Development of Knowledge Base: Possible Solutions . International Journal of Advanced Computer Science and Applications, 9 (2). doi: 10.14569/ijacsa.2018.090249
  20. Gluhih, I. N., Akhmadulin, R. K. (2017). Problem-Oriented Corporate Knowledge Base Models on the Case-Based Reasoning Approach Basis. IOP Conference Series: Materials Science and Engineering, 221, 012025. doi: 10.1088/1755-1315/221/1/012025
  21. Rashid, P. Q. (2015). Semantic Network and Frame Knowledge Representation Formalisms in Artificial Intelligence. Gazimağusa, 60. Available at: https://pdfs.semanticscholar.org/3050/f186dfd77fce3ab3d094abebd78411f5a0c1.pdf
  22. Ramirez, C., Valdes, B. (2012). A General Knowledge Representation Model of Concepts. Advances in Knowledge Representation. 2012. Available at: http://cdn.intechopen.com/pdfs/36656/InTech-A_general_knowledge_representation_model_of_concepts.pdf
  23. Panagiotopoulos, I., Kalou, A., Pierrakeas, C., Kameas, A. (2012). An Ontological Approach for Domain Knowledge Modeling and Management in E-Learning Systems. Artificial Intelligence Applications and Innovations, 95–104. doi: 10.1007/978-3-642-33412-2_10
  24. Osipov, G. S. (1997). Priobretenie znaniy intellektual'nymi sistemami. Moscow: Nauka, 345.
  25. Lyuger, D. F. (2005). Iskusstvennyy intellekt. Strategii i metody resheniya slozhnyh problem. Moscow: Vil'yams, 864.
  26. Rassel, S., Norvig, P. (2017). Iskusstvenniy intellekt: sovremennyy podhod. Moscow: Vil'yams, 1408.
  27. Allen, J. Е., Ferguson, G. (1994). Actions and events in interval temporal logic. Technical Report 521. Rochester University. Available at: https://urresearch.rochester.edu/fileDownloadForInstitutionalItem.action?itemId=609&itemFileId=736
  28. Vassilyev, S. N., Kelina, A. Y., Kudinov, Y. I., Pashchenko, F. F. (2017). Intelligent Control Systems. Procedia Computer Science, 103, 623–628. doi: 10.1016/j.procs.2017.01.088
  29. Argente, E., Julian, V., Botti, V. (2006). Multi-Agent System Development Based on Organizations. Electronic Notes in Theoretical Computer Science, 150 (3), 55–71. doi: 10.1016/j.entcs.2006.03.005
  30. Yakovlev, M. A. (2013). Ekspertnye sistemy s primeneniem dialogovogo interfeysa na estestvennom yazyke. Uchenye zametki TOGU, 4 (3), 31–39.
  31. Tarasov, V. B. Logiko-lingvisticheskie modeli v iskusstvennom intellekte: proshloe, nastoyashchee, budushchee. Available at: http://textanalysis.ru/jce/details/instrument/doc_view/186-logiko-lingvisticheskie-modeli
  32. Gavrilova, T. A., Horoshevskiy, V. F. (2001). Bazy znaniy intellektual'nyh sistem. Sankt-Peterburg: Piter, 170.
  33. Dyubua, D., Prad, A. (1990). Teoriya vozmozhnostey. Prilozheniya k predstavleniyu znaniy v informatike. Moscow: Radio i svyaz', 288.
  34. Jones, M. N., Willits, J., Dennis, S. (2015). Models of Semantic Memory. 2015. Available at: http://www.languagelearninglab.org/uploads/5/3/5/7/53575061/jones_willits_dennis_2015.pdf
  35. Grekhem, I. (2004). Ob'ektno-orientirovannye metody: principy i praktika. Moscow: Vil'yams, 879.
  36. Schank, R. C., Robert, P. A. (1977). Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. New Jersey: Lawrence Erlbaum Associates, 256. doi: 10.4324/9780203781036
  37. Gaeta, M., Orciuoli, F., Ritrovato, P. (2009). Advanced ontology management system for personalised e-Learning. Knowledge-Based Systems, 22 (4), 292–301. doi: 10.1016/j.knosys.2009.01.006
  38. Marinica, C., Guillet, F. (2010). Knowledge-Based Interactive Postmining of Association Rules Using Ontologies. IEEE Transactions on Knowledge and Data Engineering, 22 (6), 784–797. doi: 10.1109/tkde.2010.29
  39. Karapiperis, S., Apostolou, D. (2006). Consensus building in collaborative ontology engineering process. Journal of Universal Knowledge Management, 1, 199–216.
  40. Lapshin, V. (2010). Ontologiya v komp'yuternyh sistemah. Moscow: Nauchniy mir, 224.
  41. Bol'shakova, E. I., Voroncov, K. V., Efremova, N. E., Klyshinskiy, E. S., Lukashevich, N. V., Sapin, A. S. (2017). Avtomaticheskaya obrabotka tekstov na estestvennom yazyke i analiz dannyh. Moscow: Izd-vo NIU VSHE, 269.
  42. Marcenyuk, M. A. (2007). Matrichnoe predstavlenie nechetkoy logiki // Nechetkie sistemy i myagkie vychisleniya, 2 (3), 7–36.
  43. Nguen, M. H. (1993). Modelirovanie s pomoshch'yu nechetko-znachnoy veroyatnostnoy logiki. Izv. AN. Seriya: Tekhnicheskaya kibernetika, 5, 128–143.
  44. Vittih, V. A. (1999). Upravlenie otkrytymi sistemami na osnove integracii znaniy. Avtometriya, 3, 38–49.
  45. Kotis, K., Vouros, G. A. (2005). Human-centered ontology engineering: The HCOME methodology. Knowledge and Information Systems, 10 (1), 109–131. doi: 10.1007/s10115-005-0227-4

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Published

2018-03-23

How to Cite

Shostak, I., Danova, M., Romanenkov, Y., Bugaienko, O., Volk, M., & Karminska-Bielobrova, M. (2018). Organization of information support for business processes at aviation enterprises by means of ontological engineering. Eastern-European Journal of Enterprise Technologies, 2(2 (92), 45–55. https://doi.org/10.15587/1729-4061.2018.126673