Ontology-Based Approach for the Creation of Medically-Oriented Transdisciplinary Information-Analytical Platforms

Main Article Content

Ozar Mintser https://orcid.org/0000-0002-7224-4886
Oleksandr Stryzhak https://orcid.org/0000-0002-4954-3650
Vitalii Prychodniuk https://orcid.org/0000-0002-2108-7091

Keywords

Transdisciplinarity, ontology, taxonomy, medical information resources, medical knowledge systems, narrative, discourse

Abstract

The process of development of information and communication technologies and the total informatization of the health-care sector led to significant changes to quantitative and qualitative characteristics of medical information, available to broad auditory. This led to the need to create effective informational analytical platforms, that are required to cover vast arrays of polythematical informational resources that are characterized by a high degree of intensity, dynamism and diversity both in terms of content, structure, purpose, and formats, standards and creation technologies. Such platform should be used for finding, organizing and using the information needed by the users, and thus allowing them to process these arrays effectively.


One of the most useful tools for organizing all kinds of knowledge is ontology as a formal representation of some subject area. A system, capable of effective utilization of such formalization with the means of interactive documents is proposed. An example, designed for the effective finding of information based on ontologies is shown.

Abstract 192 | Ontology-Based Approach Downloads 46

References

1. Dovgyi S, Stryzhak O. Transdisciplinary Fundamentals of Information-Analytical Activity. Advances in Information and Communication
Technology and Systems. 2020:99–126. doi: 10.1007/978-3-030-58359-0_7
2. Nicolescu B. Transdisciplinarity: Theory and Practice. Cresskill, NJ: Hampton Press; 2008
3. Verhagen, WJC, Stjepandić J, Wognum N, Borsato M, Peruzzini M.. Transdisciplinary Engineering: Crossing Boundaries. Proceedings
of the 23rd ISPE Inc. International Conference on Transdisciplinary Engineering. October 3–7, 2016. Amsterdam, Netherlands: IOS Press;
2016
4. Chen C-H, Trappey A, Peruzzini M, Stjepandić Josip, Wognum N. Transdisciplinary Engineering: A Paradigm Shift: Proceedings of the 24th ISPE Inc.. International Conference on Transdisciplinary Engineering, July 10–14, 2017. Amsterdam, Netherlands: IOS Press; 2017
5. Mayer-Schönberger V, Cukier K. Big Data: A Revolution That Will Transform How We Live, Work and Think. Boston: Mariner Books; 2014
6. Hariri RH, Fredericks EM, Bowers KM. Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data. 2019;6(1). doi: 10.1186/s40537-019-0206-3
7. Mikhalevich VS, Volkovich VL. Computational methods for research and design of complex systems. Nauka; 1982
8. Kunanets NE, Pasichnyk VV. Introduction to the specialty "Consolidated information". Tutorial. Lviv: Lviv Polytechnic; 2010
9. Takashima A, Bakker I, van Hell JG, Janzen G, McQueen JM. Interaction between episodic and semantic memory networks in the acquisition and consolidation of novel spoken words. Brain and Language. 2017;167:44–60. doi: 10.1016/j.bandl.2016.05.009
10. Battaglia FP, Pennartz CM. The construction of Semantic Memory: Grammar-based representations learned from relational episodic information. Frontiers in Computational Neuroscience. 2011;5. doi: 10.3389/fncom.2011.00036
11. Barendregt X. Lambda-calculus. His syntax and semantics. Moscow : World, 1985
12. Symposium on analysis and consolidation of information (2nd meeting); September 12–15, 1978, Colombo, Sri Lanka
13. Kalytych G. I. Consolidation of information, knowledge and wisdom as planning and the basis of harmonious progress of Ukraine. NTI, 2008. No. 1. P. 51.
14. Gomez-Perez Asuncion, Fernandez-Lopez Mariano, Corcho Oscar. Ontological Engineering: With Examples from the Areas of Knowledge Management, E-commerce and the Semantic Web. Springer, 2004
15. Palagin AV. An ontological conception of informatization of scientific investigations. Cybernetics and Systems Analysis. 2016;52(1):1–7. doi: 10.1007/s10559-016-9793-6
16. Lee JD, Kirlik A. The Oxford Handbook of Cognitive Engineering. 2013. doi: 10.1093/oxfordhb/9780199757183.001.0001
17. Gladun V. Processes of formation of new knowledge. In: SD "Teacher 6";Sofia, 1994
18. Aksu-Koç A, Aktan-Erciyes A. 16. Narrative discourse: Developmental perspectives. Handbook of Communication Disorders. 2018:329–356. doi: 10.1515/9781614514909-017
19. Elson DK. Modeling Narrative Discourse. New York City: Columbia University; 2012
20. Guajardo NR, Watson AC. Narrative discourse and theory of mind development. The Journal of Genetic Psychology. 2002;163(3):305–325. doi: 10.1080/00221320209598686
21. Velichko VYu. Logical-linguistic models as a technological basis of interactive knowledge bases. International Journal "Information Models and Analyses". 2019;8(4)
22. Gonchar AV, Stryzhak OE, Berkman LN. Transdisciplinary consolidation of information environments. Communication. 2021;1(149):3–9
23. Homotopy Type Theory: Univalent Foundations of Mathematics. Institute for Advanced Study. Princeton, 2013
24. Nadutenko MV. Virtualized lexicographical systems and their application in applied linguistics. National Academy оf Sciences оf Ukraine, Vernadskyi National Library of Ukraine. 2016. Accessed July 7, 2021. https://ulif.mon.gov.ua/system/files/nadutenko_aref.pdf