Авторы: 
Cherkashin Evgeny Alexandrovich
Popova Victoria Alekseyevna
Дата поступления: 
21.11.2022
Библиографическое описание статьи: 

Cherkashin E.A., Popova V.A. Knowledge graph based distributed infrastructure for processing education process documents // Informacionnye tehnologii i matematicheskoe modelirovanie v upravlenii slozhnymi sistemami: ehlektronnyj nauchnyj zhurnal [Information technology and mathematical modeling in the management of complex systems: electronic scientific journal], 2022. No. 4(16). P. 44-55. DOI: 10.26731/2658-3704.2022.4(16).44-55 [Accessed 17/12/22].

Рубрика: 
3. Information Technology in Education
Год: 
2022
Номер журнала (Том): 
4(16) 2022
УДК: 
004.89
DOI: 

10.26731/2658-3704.2022.4(16).44-55 

Файл статьи: 
Иконка PDF knowledge_graph.pdf
Страницы: 
44
55
Аннотация: 

The article deals with the application of the author's infrastructure components based on the representation of data in the knowledge graph and its rule-based processing. The components are used to create an environment for processing university course documents, including their reconstruction from PDF, storage, authoring based on the stored data. The information accumulated in the knowledge graph forms a platform for the automation of the educational process. The main goal of the R&D is to develop algorithms and software to integrate static data from the university website presented in the form of working programs of disciplines with the university information infrastructure, such as library, existing process planning systems previously developed undergraduates and faculty of the university departments.

Ключевые слова: 
knowledge graph
logical inference
education process automation
distributed data processing
document authoring automation
Список цитируемой литературы: 
  1. Stojanov Z., Stojanov J., Jotanovic G., Dobrilovic D. Weighted networks in socio-technical systems: Concepts and challenges. CEUR-WS Proceedings of the 2nd International Workshop on Information, Computation, and Control Systems for Distributed Environments Irkutsk, Russia, July 6-7, 2020, pp. 265–276.
  2. Hogan A., Blomqvist E., Cochez M., D’Amato C. et al. Knowledge Graphs. 2020,  URL:https://arxiv.org/abs/2003.02320v5 (access date: 12-Dec-2021)
  3. Erling O. Virtuoso, a Hybrid RDBMS/Graph Column Store. IEEE Data Eng. Bull, 2012. vol. 35 – pp. 3–8.
  4. Wielemaker J., Beek W., Hildebrand M., Ossenbruggen J. ClioPatria: A SWI-Prolog infrastructure for the Semantic Web. Semantic Web. 2016, vol. 7(5), pp. 529–541
  5. Berners-Lee T., Hendler J., Lassila O. The semantic web: A new form of web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, May 2001.
  6. Lager T., Wielemaker J. Pengines: Web Logic Programming Made Easy. Theory and Practice of Logic Programming. 2014, no. 4-5, vol. 14, pp. 539–552.
  7. Wielemaker J., Schreiber G., Wielinga B., Prolog-based infrastructure for RDF: scalability and performance. In: D. Fensel, K. Sycara, J. Mylopoulos (eds) The Semantic Web – ISWC 2003. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. 2003, vol. 2870.
  8. Wielemaker J., Schrijvers T., Triska M., Lager T. SWI-Prolog. Theory and Practice of Logic Programming, 2011, no. 2, vol. 2, pp. 67–96, ISSN 1471-0684.
  9. Cherkashin E., Shigarov A., Paramonov V., Mikhailov A. Digital archives supporting document content inference. Procs. of 42-nd International Convention on Information and Communication Technology Electronics and Microelectronics (MIPRO), May 20–24, 2019, pp. 1037-1042.
  10. Cherkashin E., Shigarov A., Paramonov V. Representation of MDA transformation with logical objects. Procs. of International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) Novosibirsk, Russia, 2019, pp. 0913–0918
  11. Bizer Ch., Heath N., Berners-Lee T., Linked data – the story so far. International Journal on Semantic Web and Information Systems. 2009, vol. 5 (3),  pp. 1–22.
  12. Heino N., Tramp S., Heino N., Auer S., Managing web content using linked data principles – combining semantic structure with dynamic content syndication. Computer Software and Applications Conference (COMPSAC), IEEE 35th Annual, 2011, pp. 245–250.  URL:http://svn.aksw.org/papers/2011/COMPSAC_lod2.eu/public.pdf
  13. Kuć R., Rogoziński M. Mastering Elasticsearch - Second edition, Packet Publishing. 2015, 372 p.
  14. Cherkashin E., Terehin I., Paramonov V. New transformation approach for Model Driven Architecture. Proceedings of the 35th International Convention MIPRO, Opatija, Choatia, 2012, pp. 1082-1087.
  15. Lehmann J., Isele R., Jakob M., Jentzsch A., Kontokostas D., et al, DBpedia – a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web Journal. IOS Press. 2015, no. 2, vol. 6 pp. 167–195
  16. Moura P. Programming Patterns for Logtalk Parametric Objects. In: Abreu, S., Seipel, D. (eds) Applications of Declarative Programming and Knowledge Management. INAP-2009.  Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. 2011, vol. 6547.
  17. A LuaLaTeX class for authoring course description. URL:https://github.com/eugeneai/sucourse (access date: 10.10.2022)
  18. ETU “LETI”. URL:https://etu.ru/en/university/ (access date: 10.10.2022)
  19. INRTU is a university with the best traditions… URL:https://eng.istu.edu/ (access date: 10.10.2022)
  20. LMS Lan’. URL:https://lanbook.com/ (in Russian) (access date: 10.10.2022)
  21. Shigarov A., Paramonov V., Belykh P., Bondarev A. Rule-based canonicalization of arbitrary tables in spreadsheets. In: Dregvaite G., Damasevicius R. (eds) Information and Software Technologies. ICIST 2016.
  22. Shigarov A., Mikhailov A. Rule-based spreadsheet data transformation from arbitrary to relational tables. Information Systems, 2017, vol. 71.