ONTOLOGICAL BASIS OF KNOWLEDGE MANAGEMENT IN THE CONTEXT OF EDUCATIONAL AND RESEARCH ACTIVITY

Authors

  • V. V. Prykhodniuk PhD in Engineering, Department Head of Department of creating and using intelligent networking tools, the NC “Junior Academy of Sciences of Ukraine”, Kyiv, Ukraine, [email protected]; ORCID ID: https://orcid.org/0000-0002-2108-7091
  • V. V. Gorborukov PhD in Engineering, Researcher of Department of creating and using intelligent networking tools, the NC “Junior Academy of Sciences of Ukraine”, Kyiv, Ukraine, [email protected]; ORCID ID: https://orcid.org/0000-0002-2758-7724
  • O. V. Franchuk PhD in Engineering, Senior Researcher of Department of creating and using intelligent networking tools, the NC “Junior Academy of Sciences of Ukraine”, Kyiv, Ukraine, [email protected]; ORCID ID: https://orcid. org/0000-0002-1122-4689

DOI:

https://doi.org/10.51707/2618-0529-2023-28-07

Keywords:

knowledge management, knowledge representation, ontological engineering, cognitive services.

Abstract

The main problem in organizing the transfer of knowledge in scientific and educational activities is to extract it from data and information, as well as to effectively present it to the end user in a form that is convenient for perception. The exponential growth of the volumes of information and data and their accumulation in spatially distributed databases and repositories has created the need to use new methods and tools that allow to significantly increase the efficiency of the processes of extraction and transformation into knowledge. At the same time, the main goal is to ensure high quality of educational and scientific materials, which, in turn, depends on the volumes of information sources processed by teachers and researchers. Simultaneously, the problem is the fragmentation and dispersion of information, which significantly complicates its holistic perception. The solution is the application of ontological models, as one of the most modern techniques of knowledge representation. Therefore, it is quite appropriate to use ontological and ontology-controlled approaches in the processes of knowledge transfer. An option of the implementation of such an approach based on cognitive services that implement the processes of creating annotations, extracting named entities, generalizing terms, determining the semantic proximity of terms, categoriz­ing and classifying terms, and full-text search is proposed. As a result, a high level of efficiency in the presentation of educational and scientific materials is achieved for their successful use and perception.

References

Moradi, M., & Vallespir, B. (2007). Knowledge Management and Enterprise Modelling: a Complementary View. IFAC Proceedings Volumes, 40 (18), 67–72. DOI: https://doi.org/10.3182/20070927-4-RO-3905.00013.

Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and Leadership: a Unified Model of Dynamic Knowledge Creation. Long Range Planning, 33 (1), 5–34. DOI: https://doi.org/10.1016/S0024-6301(99)00115-6.

Kakabadse, N. K., Kakabadse, A., & Kouzmin, A. (2003). Reviewing the knowledge management literature: towards a taxonomy. Journal of Knowledge Management, 7 (4), 75–91. DOI: https://doi.org/10.1108/13673270310492967.

Kordab, M., & Raudeliuniene, J. (2018). Knowledge management cycle: a scientific literature review. Business and Management 2018 : 10th International Scientific Conference. (рр. 140–149). DOI: https://doi.org/10.3846/bm.2018.16.

Raudeliuniene, J., Davidaviciene, V., & Jakubavicius, A. (2018). Knowledge management process model. Entrepreneurship and Sustainability Issues, 5 (3), 542–554. DOI: https://doi.org/10.9770/jesi.2018.5.3(10).

Raudeliuniene, J., & Meidute-Kavaliauskiene, I. (2016). Editorial: special issue on knowledge management: theory and practice in SMEs. International Journal of Learning and Change, 8 (3/4), 193–197.

Riswanto, & Sensuse, D. I. (2021). Knowledge Management Systems Development and Implementation: A systematic Literature Review. IOP Conference Series: Earth and Environmental Science, 704 (1), 1–11. DOI: https://doi.org/10.1088/1755-1315/704/1/012015.

Ranjbarfard, M., Aghdasi, M., Lopez-Saez, P., & Emilio Navas Lopez, J. (2014). The barriers of knowledge generation, storage, distribution and application that impede learning in gas and petroleum companies. Journal of Knowledge Management, 18 (3), 494–522. DOI: https://doi.org/10.1108/JKM-08-2013-0324.

Ale, M. A., Toledo, C. M., Chiotti, O., & Galli, M. R. (2014). A conceptual model and technological support for organizational knowledge management. Science of Computer Programming, 95 (1), 73–92. DOI: https://doi.org/10.1016/j.scico.2013.12.012.

Ackerman, M. S. (1996). Definitional and contextual issues in organizational and group memories. Information Technology & People, 9 (1), 10–24. DOI: https://doi.org/10.1108/09593849610111553.

Jennex, M. E., & Olfman, L. (2002). Organizational memory / knowledge effects on productivity, a longitudinal study. Proceedings of the 35th Annual Hawaii International Conference on System Sciences. (рр. 1029–1038). DOI: https://doi.org/10.1109/HICSS.2002.994053.

Alavi, M., & Leider, D. (1999). Knowledge management systems: emerging views and practices from the field. Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. (р. 8). DOI: https://doi.org/10.1109/HICSS.1999.772754.

Davenport, T. H., De Long, D. W., & Beers, M. C. (1998). Successful Knowledge Management Projects. Sloan Management Review, 39 (2), 43–57.

SIMA, X., Coudert, T., Geneste, L., & de Valroger, A. (2022). Knowledge management in SMEs: preliminary ideas for a dedicated framework. IFAC-PapersOnLine, 55 (10), 1050–1055. DOI: https://doi.org/10.1016/j.ifacol.2022.09.528.

Yu, S.-H., Kim, Y.-G., & Kim, M.-Y. (2004). Linking organizational knowledge management drivers to knowledge management performance: an exploratory study. 37th Annual Hawaii International Conference on System Sciences. (рр. 1–10). DOI: https://doi.org/10.1109/HICSS.2004.1265572.

Wong, K. Y., & Aspinwall, E. (2005). An empirical study of the important factors for knowledge‐management adoption in the SME sector. Journal of Knowledge Management, 9 (3), 64–82. DOI: https://doi.org/10.1108/13673270510602773.

Sage, A. P., & Rouse, W. B. (1999). Information Systems Frontiers in Knowledge Management. Information Systems Frontiers, 1 (3), 205–219. DOI: https://doi.org/10.1023/A:1010046210832.

Stryzhak, O., Prykhodniuk, V., Popova, M., Nadutenko, M., Haiko, S., & Chepkov, R. (2021). Development of an Oceanographic Databank Based on Ontological Interactive Documents. Lecture Notes in Networks and Systems, 97–114. DOI: https://doi.org/10.1007/978-3-030-80126-7_8.

Nadutenko, M., Prykhodniuk, V., Shyrokov, V., & Stryzhak, O. (2022). Ontology-Driven Lexicographic Systems. Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, 204–215. DOI: https://doi.org/10.1007/978-3-030-98012-2_16.

Lin, X. (2019). Review of Knowledge and Knowledge Management Research. American Journal of Industrial and Business Management, 9 (9), 1753–1760. DOI: https://doi.org/10.4236/ajibm.2019.99114.

Published

2024-01-27

How to Cite

Prykhodniuk, V. V., Gorborukov, V. V., & Franchuk, O. V. (2024). ONTOLOGICAL BASIS OF KNOWLEDGE MANAGEMENT IN THE CONTEXT OF EDUCATIONAL AND RESEARCH ACTIVITY. Scientific Notes of Junior Academy of Sciences of Ukraine, (3(28), 62–69. https://doi.org/10.51707/2618-0529-2023-28-07