Approaches to the construction and use of an ontology of an intelligent risk management system domain

Olga Baranova, Nikita Goglev, Sergey Migalin, Oksana Mushtak

Abstract


For risk management decisions to be objective and effective, they must be based on accurate data, presented in a timely manner and processed from a variety of sources. The basis for the integration of such data is an ontology that provides a basis for building risk assessment models using various artificial intelligence methods and is capable of continuously evolving in response to changes in the internal and external environment of the organization. Approaches to building an end-to-end automated process of creating and developing an ontology of a risk management system domain based on methods proven by world practice are proposed. The proposed approaches cover the main tasks of ontological engineering, taking into account the specifics of risk management, incl. (1) conceptualization of the subject area in relation to the problems of risk management, (2) ontological reengineering based on the conceptualization of the legacy risk management system and the study of the historical array of results of the application of such a system, (3) assessment of the quality of the ontology, (4) evolution of the ontology in response to changes internal and external environment of the organization. Examples of ontology application in the intellectual risk management system are presented.


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References


GOST R ISO 31000-2019 Nacional'nyj standart Rossijskoj Federacii. Menedzhment riska. Principy i rukovodstvo.

GOST R 51897-2021 (ISO Guide 73:2009) Menedzhment riska. Terminy i opredelenija.

GOST R 58771-2019 Nacional'nyj standart Rossijskoj Federacii. Menedzhment riska. Tehnologii ocenki riska.

Ukaz Prezidenta RF ot 10.10. 2019 # 490 «O razvitii iskusstvennogo intellekta v Rossijskoj Federacii» [Jelektronnyj resurs]. URL: http://static.kremlin.ru/media/events/files/ru/AH4x6HgKWANwVtMOfPDhcbRpvd1HCCsv.pdf (data obrashhenija: 30.03.2022).

Artificial Intelligence (AI) Applied to Risk Management [Jelektronnyj resurs]. URL: https://www.ferma.eu/app/uploads/2019/11/FERMA-AI-applied-to-RM-FINAL.pdf (data obrashhenija: 30.03.2022).

Nasibullin A.A. Upravlenie riskami v uslovija intellektualizacii cifrovyh tamozhennyh tehnologij // Vestnik Rossijskoj tamozhennoj akademii. 2021 g. # 1. S. 153-159.

Mark L'vov. Upravlenie riskami stanet prozrachnym. Iskusstvennyj intellekt prismotrit za sobljudeniem pravil bezopasnosti dvizhenija [Jelektronnyj resurs] // Gudok, vypusk # 21 (27357), 08.02.2022. URL: https://gudok.ru/newspaper/?ID=1594450&archive=2022.02.08 (data obrashhenija: 30.03.2022).

Ontologicheskoe modelirovanie predprijatij: metody i tehnologii: monogr. / S. V. Gorshkov, S. S. Kralin, O. I. Mushtak i dr.; otv. red. S. V. Gorshkov. – Ekaterinburg: Izdatel'stvo Ural'skogo universiteta, 2019. – 234 s.

Ria Andryani, Edi Surya Negara. Survey on Development Method of Ontology // The 4th ICIBA 2015, International Conference on Information Technology and Engineering Application. Palembang-Indonesia, 20-21 February 2015.

C. Maria Keet. An Introduction to Ontology Engineering. V1.5. 2020. 289 p. URL: https://people.cs.uct.ac.za/~mkeet/files/OEbook.pdf (data obrashhenija: 30.03.2022).

Kotis, K., Vouros, G., & Spiliotopoulos, D. Ontology engineering methodologies for the evolution of living and reused ontologies: Status, trends, findings and recommendations // The Knowledge Engineering Review. 2020. Vol. 35, E4. doi:10.1017/S0269888920000065.

Gomez-Perez A, Fernandez-Lopez M., Corcho O. Ontological Engineering. Springer Verlag, 2004.

Uschold M., King M., Moralee S., Zorgios Y. (1998). The Enterprise Ontology // Knowl. Eng. Rev. 1998. Vol. 13(1). P. 31-89. doi:10.1017/S0269888998001088.

Gruninger M., Fox M. S. Methodology for the design and evaluation of ontologies. In IJCAI Workshop on Basic Ontological Issues in Knowledge Sharing, 1995.

About NeON// NeOn Project [Jelektronnyj resurs]. URL: http://neon-project.org/nw/About_NeOn.html (data obrashhenija: 30.03.2022).

Alexander Garcia, Kieran O’Neill, Leyla Jael Garcia, Phillip Lord, Robert Stevens, Oscar Corcho, and Frank Gibson. Developing ontologies within decentralized settings. In H. Chen et al., editors, Semantic e-Science. Annals of Information Systems 11, pages 99–139. Springer, 2010.

Gruber T. R. Toward Principles for the Design of Ontologies Used for Knowledge Sharing // International Journal Human-Computer Studies. – 1995. – # 43. – pp. 907-928.

Mogilko D.Ju. Upravlenie riskami: model' processa i kompetencij // Menedzhment kachestva. 2019. No3. S.184–199. URL: https://grebennikon.ru/article-tfn3.html (data obrashhenija: 30.03.2022).

OWL 2 Web Ontology Language Structural Specification and Functional-Style Syntax (Second Edition) [Jelektronnyj resurs] / W3C. 2012. URL: https://www.w3.org/TR/owl2-syntax/ (data obrashhenija: 30.03.2022).

Protégé [Jelektronnyj resurs]. URL: https://protege.stanford.edu/ (data obrashhenija: 30.03.2022).

Gomez-Perez A., Rojas M.D. Ontological Reengineering and Reuse // In: Proc. of 11th European Workshop on Knowledge Acquisition, Modelling and Management. – Germany: Springer-Verslag, Dagstuhl Castle, 1999, pp. 139–156.

MDA® - The Architecture Of Choice For A Changing World / OMG. URL: https://www.omg.org/mda/index.htm (data obrashhenija: 30.03.2022).

C. Partridge. Business Objects: Re-Engineering for Re-Use [2nd Edition] // BORO Centre. 2005. ISBN 0-9550603-0-3. URL: http://www.boroprogram.org/boro_program/bo_rfr.htm (data obrashhenija: 11.03.2022).

Raad, Joe & Cruz, Christophe. A Survey on Ontology Evaluation Methods // Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Lisbon, Portugal. 2015. doi:10.5220/0005591001790186.

Zablith F., Antoniou G., D’Aquin M., Flouris G., Kondy-lakis H., Motta E., Plexousakis D., Sabou M. Ontology evolution: A process-centric survey // Knowl. Eng. Rev. 2015. Vol. 30, no. 1. P. 45-75.

F. Zablith. Dynamic Ontology Evolution // International Semantic Web Conference (ISWC) Doctoral Consortium. – Karlsruhe, Germany, 2008.

P. Haase, L. Stojanovic. Consistent Evolution of OWL Ontologies // The Semantic Web: Research and Applications // Proceedings of the 2-nd European Semantic Web Conference (ESWC). Lecture Notes in Computer Science. Berlin: Springer-Verlag, 2005. Vol. 3532, p 182–197.

Lykourentzou, I. & Papadaki, K. & Kalliakmanis, A.postolis & Djaghloul, Y. & Latour, Thibaud & Charalabis, Ioannis & Kapetanios, Epaminondas. (2011). Ontology-based Operational Risk Management. Proceedings - 13th IEEE International Conference on Commerce and Enterprise Computing, CEC 2011. 153 - 160. 10.1109/CEC.2011.18.

H. Mizen, et al., Ontology Ontogeny: Understanding How an Ontology is Created and Developed, Springer, 2005.

Franco Martins Souza, B.; Serrano Gil, LJ.; Reyes Román, JF.; Panach Navarrete, JI.; Pastor López, O. (2021). Towards the Consolidation of Cybersecurity Standardized Definitions. Universitat Politècnica de València.

Ekelhart, Andreas & Fenz, Stefan & Neubauer, Thomas. (2009). Ontology-Based Decision Support for Information Security Risk Management. Proceedings of the 4th International Conference on Systems, ICONS 2009. 80-85. 10.1109/ICONS.2009.8.

A. Ekelhart, S. Fenz and T. Neubauer, "AURUM: A Framework for Information Security Risk Management," 2009 42nd Hawaii International Conference on System Sciences, 2009, pp. 1-10, doi: 10.1109/HICSS.2009.82.

Jennifer O. Contreras, Melvin A. Ballera, and Enrique D. Festijo. 2020. Ontology Learning using Hybrid Machine Learning Algorithms for Disaster Risk Management. In Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning (SPML 2020). Association for Computing Machinery, New York, NY, USA, 13–20.


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