Информационные технологии
N.E. Kalenov, I.N. Sobolevskaya, A.N. Sotnikov "Classes of objects and relations in the Common Digital Space of Scientific Knowledge"
Интеллектуальный анализ данных
Методы и модели в естественных науках
Компьютерный анализ текстов
N.E. Kalenov, I.N. Sobolevskaya, A.N. Sotnikov "Classes of objects and relations in the Common Digital Space of Scientific Knowledge"
Abstract. 

All over the world there are many both global and local information systems focused on solving various problems. As an integrator that allows you to solve complex information problems at the intersection of sciences and application areas of existing information systems. to the maximum extent using the information resources accumulated in them, the Common Digital Space of Scientific Knowledge (CDSSK) can be considered. The article provides the structure of the CDSSK, the requirements for its functionality and the structure of the software shell, corresponding to the principles of the Semantic WEB. All objects reflected in the CDSSK are divided into two classes – universal and local. Relationships between objects are also divided into two groups – universal and specific. The paper proposes a list of universal classes of objects, defines universal types of relations between them, gives examples of specific relations and approaches to identifying local classes and subclasses of objects in a particular field of science.

Keywords: 

сommon Digital Space of Scientific Knowledge, Semantic WEB, codification, objects classes, object reference, object relations, entity reference, relationships between objects, ontology.

Стр. 4-8.

DOI: 10.14357/20790279230101
 
 
References

1. Antopolskij A.B., Kalenov N.E., Serebryakov V.A., Sotnikov A.N. O edinom cifrovom prostranstvenauchnyh znanij // Vestnik Rossijskoj akademii nauk, 2019. – T. 89, – № 7. – S. 728-735. DOI 10.31857/S0869-5873897728-735
2. Savin G.I. Edinoe cifrovoe prostranstvo nauchnyh znanij: celi i zadachi // Informacionnye resursy Rossii, 2020. – № 5. – S. 3-5. DOI: 10.51218/0204- 3653-2020-5-3-5
3. Nikolay Kalenov, Gennadiy Savin, Alexander Sotnikov. Fundamentals of Common Digital Space of Scientific Knowledge Building // CEUR Workshop Proceedings (CEUR-WS.org) , 2021. – Vol. 2990. – P. 93-99. DOI: 10.51218/1613-0073-2990-93-99
4. Olga Ataeva, Nikolay Kalenov, Vladimir Serebryakov, Alexander Sotnikov. Informational Infrastructure of the Common Digital Space of Scientific Knowledge // CEUR Workshop Proceedings (CEUR-WS.org) , 2021. – Vol. 2990. – P. 1-10. DOI: 10.51218/1613-0073-2990-1-10
5. https://bigenc.ru/ (the last access 12.2021)
6. https://rusneb.ru/ (the last access 12.2021)
7. https://fedresurs.ru/?attempt=1 (the last access 12.2021)
8. https://cgkipd.ru/science/names/reestry-gkgn.php (the last access 12.2021)
9. https://elibrary.ru/project_risc.asp? (the last access 12.2021)
10. Millar D., Braines D., D’Arcy L., Barclay I., Summers-Stay D., Cripps P. Embedding Dynamic Knowledge Graphs based on Observational Ontologies in Semantic Vector Spaces // Artificial intelligence and machine learning for multi-domain operations applications III. Vol. 11746., № 117461O. (2021).
11. Wang Q., Ji YD., Hao YS., Cao J. GRL: Knowledge graph completion with GAN-based reinforcement learning // Knowledge-based systems. Vol. 209., № статьи 106421. (2020).
12. Hansen C., Hotz I., Ynnerman A. Visualization in Public Spaces // Ieee computer graphics and applications. 40 (2). pp. 16-17. (2020).
13. Piplai A., Ranade P., Kotal A., Mittal S., Narayanan SN., Joshi A. Using Knowledge Graphs and Reinforcement Learning for Malware Analysis // 2020 IEEE international conference on big data (big data). pp. 2626-2633. (2020).
14. Dessi D., Osborne F., Recupero DR., Buscaldi D., Motta E. Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain // Future generation computer systems-the international journal of escience. Vol. 116. pp. 253-264. (2021).
15. Nikolay Kalenov, Irina Sobolevskaya, Alexander Sotnikov. Hierarchical Representation of Information Objects in a Digital Library Environment // Communications in Computer and Information Science. Vol. 1093. pp. 93-104 (2019).
16. UDC: https://udcc.org/index.php/site/page?view=factsheet (the last access 12.2021)
17. INIS: https://www.iaea.org/sites/default/files/19/09/en-2019-09.pdf (the last access 12.2021)
18. SRSTI: https://grnti.ru (the last access 12.2021) 
2024-74-1
2023-73-4
2023-73-3
2023-73-2

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".