Izvestiya vuzov. Yadernaya Energetika

The peer-reviewed scientific and technology journal. ISSN: 0204-3327

Semantic web and knowledge graphs as an educational technology of personnel training for nuclear power engineering

6/24/2019 2019 - #02 Personnel training

Telnov V.P. Korovin Yu.A.

DOI: https://doi.org/10.26583/npe.2019.2.19

UDC: 621.039:004.8

The paper is devoted to knowledge representation technologies, reasoning models and methods of generating cognitive hypotheses in artificial intelligence systems. Practical emphasis is placed on the use of problem-oriented knowledge graphs as an educational technology in the training of specialists in the field of nuclear physics and nuclear energy. The proposed information solutions and approaches cover the tasks of computer detection and classification of nuclear knowledge and competences on the basis of ontologies, semantic annotation tools for full-text network content and representation of information objects in graph databases equipped with logical inference tools. The applied part of the project are presented publicly available semantic web portal which includes prototypes for the following graphs of nuclear knowledge: the world centre of nuclear data; events and publications CERN; databases and network services of the IAEA; educational materials of the MSU and MEPhI in nuclear physics; nuclear research centers of the Russian Federation,;journals for nuclear physics; the joint graph of nuclear knowledge. Interactive visual navigation through knowledge graphs is performed using special search widgets and an intelligent RDF browser. The RDF browser allows users to make visual tours of knowledge graphs in any direction and at any distance, extracting the necessary information in the form of metadata, hypertext links, full-text and media content associated with a particular node of the graph. Operations with semantic repositories are performed on cloud platforms using PaaS and DBaaS service models, which provides scalability of the involved data stores and network services. The results of performance testing of the semantic base of nuclear knowledge and metric of the computational processes are presented. The innovative potential of the proposed solutions in relation to educational activities is considered.


  1. Harmelen F., Lifschitz V., Porter B. Handbook of Knowledge Representation. England, Oxford: Elsevier Science Oxford, 2008, ISBN 78-0-444-52211-5.
  2. W3C RDF Schema 1.1. Available at: http://www.w3.org/TR/rdf-schema (accessed Mar 25, 2019).
  3. W3C OWL 2 Web Ontology Language. Available at: http://www.w3.org/TR/owl2-overview (accessed Mar 25, 2019).
  4. Baader F., Calvanese D., McGuinness D., Nardi D., Patel-Schneider P. The Description Logic Handbook: Theory, Implementation and Applications, 2nd Ed. New York, USA, Cambridge University Press, 2010, ISBN 0521150116 9780521150118.
  5. W3C Semantic Web. Available at: http://www.w3.org/standards/semanticweb (accessed Mar 25, 2019).
  6. Proceedings of the 14th International Conference on Semantic Systems. Vienna, Austria, 10–13 September 2018. Available at: http://www.sciencedirect.com/journal/procedia-computer-science/vol/137 (accessed Mar 25, 2019).
  7. Proceedings of the 31st International Workshop on Description Logics. Tempe,Arizona, US, 27-29 October, 2018. Available at: http://ceur-ws.org/Vol-2211/ (accessed Mar 25, 2019).
  8. Knowledge Management for Nuclear Research and Development Organizations, IAEA TECDOC No. 1675. Available at: http://www-pub.iaea.org/books/IAEABooks/8644/Knowledge-Management-for-Nuclear-Research-and-Development-Organizations (accessed Mar 25, 2019).
  9. Leshchenko V. Rosatom knowledge management. History of success. Available at: http://www.imodel-russia.com/upload/MI_dokladi/MI_Rosatom_Leshchenko_NEOLANT_Kutilin.pdf/ (accessed Mar 25, 2019) (in Russian).
  10. The semantic Base of Nuclear Knowledge. Available at: http://vt.obninsk.ru/x/ (accessed Mar 25, 2019) (in Russian).
  11. Educational portal of NRNU MEPhI. Available at: http://online.mephi.ru/ (accessed Mar 25, 2019) (in Russian).
  12. Howard P. Graph and RDF databases. Market Report Paper by Bloor 2018. Available at: http://www.bloorresearch.com/technology/graph-databases (accessed Mar 25, 2019).
  13. The University of Manchester. List of Reasoners. Available at: http:// owl.cs.manchester.ac.uk/tools/list-of-reasoners (accessed Mar 25, 2019).
  14. D’Amato C., Fanizzi N., Fazzinga B., Gottlob G., Lukasiewicz T. Combining Semantic Web Search with the Power of Inductive Reasoning (2013). Available at: http://ceur-ws.org/Vol-527/paper2.pdf (accessed Mar 25, 2019).
  15. Minervini P., d’Amato C., Fanizzi N., Tresp V., Discovering Similarity and Dissimilarity Relations for Knowledge Propagation in Web Ontologies. Journal on Data Semantics. 2016, v. 5, iss. 4, December, pp. 229-248.
  16. Telnov V.P. The Contextual Search as a Technique for Extracting Knowledge on the Internet. Programmnaya Inzheneriya. 2017, no. 1, pp. 26-37 (in Russian).
  17. Telnov V.P., Korovin Yu.А. Programming of Knowledge Graphs, Reasoning on Graphs. Programmnaya Inzheneriya. 2019, no. 2, pp. 59-68 (in Russian).
  18. Reasoners and rule engines: Jena inference support. Available at: http:// jena.apache.org/documentation/inference (accessed Mar 25, 2019).
  19. Amazon Web Services. Available at: http://aws.amazon.com/ru (accessed Mar 25, 2019).
  20. An example of ontology «Educational materials of Moscow State University and NRNU MEPhI on Nuclear Physics». Available at: http://drive.google.com/file/d/1AIXMsm3cfAxR6NX220R4ZeFeoSFp0mj5 (accessed Mar 25, 2019) (in Russian).

semantic web educational portal ontology graph database knowledge graph cloud computing