Knowledge Representation in Multilingual Education Resources

Research Article
EDN: FHBBDY DOI: 10.31483/r-104718
Open Access
International academic journal «Development of education». Volume 5
Creative commons logo
Published in:
International academic journal «Development of education». Volume 5
Authors:
Eugeniya M. Volegzhanina 1 , Irina S. Volegzhanina 1
Work direction:
Pedagogy and Modern Education
Pages:
19-26
Received: 5 December 2022 / Accepted: 27 December 2022 / Published: 28 December 2022

Rating:
Article accesses:
1641
Published in:
doaj РИНЦ
1 Siberian Transport University
For citation:
UDC 372.862

Abstract

Introduction. Ontologies are now recognised as the advanced standard of knowledge representation for e-learning and some industries. In particular, the development of multilingual ontological education resources is characterised as a promising area of research in the context of industry universities’ digital transformation. The article deals with the development of an academic course multilingual ontology in a Controlled Natural Language. Relevance. Although there are many ontology editors, national developers of education resources should be familiar with formal logic and have a good command of English. Therefore, it is difficult to discuss widespread use of ontology-based education solutions in Russian universities. Materials and Methods. The article offers a version of Controlled Russian Language for academic knowledge representation. A methodology to be used for compiling academic course ontologies is developed. As an example, a piece of ontology for the Introductory Course on Railways is considered. Results and Discussion. To support this way of knowledge representation, a prototype of ontology editor Onto.plus was developed to support the version of Controlled Russian Language. To implement the multilanguage function, equivalent versions for the Controlled Russian Language ontology were developed in English and Chinese. Conclusions. The solutions are a contribution to the implementation of an open project to develop an ontology resource integrating universities and industry.

References

  1. 1. Aho, A.V., Ullman, J.D. (1972). The theory of parsing, translation, and compiling : Parsing. Prentice-Hall, 542 p.
  2. 2. Balandina, A., Kostkina, A., Chernyshov, A., Klimov, V. (2018). Dependency Parsing of Natural Russian Language with Usage of Semantic Mapping Approach, Procedia Computer Science, 145: 77–83, DOI: 10.1016/j.procs.2018.11.013
  3. 3. Briola, D., Caccia, R., Bozzano, M., Locoro, A. (2013). Ontologica: Exploiting ontologies and natural language for railway management. Design, implementation and usage examples. International Journal of Knowledge-Based and Intelligent Engineering Systems, 17(1): 3–15, DOI:10.3233/KES-130262.
  4. 4. Edler, J. (2003). Knowledge Management in German Industry: Final report. Fraunhofer Institute for Systems and Innovation Research (ISI), Karlsruhe, 88 р., DOI: 10.1787/9789264100282-5-en
  5. 5. Gonzalez-Perez, C [et al.] (2016). An Ontology for ISO software engineering standards: Proof of concept and application Computer Standards & Interfaces, 48: 112–123, DOI: https://doi.org/10.1016/j.csi.2016.04.007
  6. 6. Hastings, J [et al.] (2021). Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification, Journal of Cheminformatics, 13(23), DOI:10.1186/s13321-021-00500-8.
  7. 7. Hunter, R. (1981). The design and construction of compilers, Chichester ; New York : Wiley, 272 p.
  8. 8. Kaljurand, K., Kuhn, T. (2013). A Multilingual Semantic Wiki Based on Attempto Controlled English and Grammatical Framework, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 7882, DOI:https://doi.org/10.1007/978-3-642-38288-8_29
  9. 9. Khabarov, V., Volegzhanina, I. (2018). Training of transport industry personnel in the digital economy: the evolution of information educational technology, MATEC Web of Conferences. Siberian Transport Forum – TransSiberia 2018, 239:1–11, DOI: 10.1051/matecconf/201823907001
  10. 10. Kuhn, T. (2014). A Survey and Classification of Controlled Natural Languages, Computational Linguistics, 40(1):121–171, DOI: 10.1162/COLI_a_00168
  11. 11. Kuznetsova, A. (2020). Problems and prospects of the Russian language in modern multicultural space, European Proceedings of Social and Behavioural Sciences, 92:3719–3725, DOI: https://doi.org/10.15405/epsbs.2020.10.05.495
  12. 12. Mohd Zulkifli, R [et al.] (2018). Industry-specific knowledge that vocational teachers should know and be able to do to prepare a job-ready workforce, Journal of Engineering Science and Technology, 13: 14–22.
  13. 13. Norbert, E. Fuchs (2010). Controlled Natural Language, Workshop on Controlled Natural Language, CNL 2009. Marettimo Island, Italy, June 8–10, 2009, Springer-Verlag Berlin Heidelberg, DOI: https://doi.org/10.1007/978-3-642-14418-9
  14. 14. Volegzhanina, I.S. (2016), Linguotranslation aspect of the development of a multilingual ontology of a specialist academic discipline in Russian Linguistic Bulletin, 4(8):93–95, DOI: 10.18454/RULB.8.09
  15. 15. Winkler, K., Kuhn, T. (2017). Fully automatic multi-language translation with a catalogue of phrases: successful employment for the Swiss avalanche bulletin, Lang Resources & Evaluation, 51:13–35, DOI:https://doi.org/10.1007/s10579-015-9316-5

Comments(0)

When adding a comment stipulate:
  • the relevance of the published material;
  • general estimation (originality and relevance of the topic, completeness, depth, comprehensiveness of topic disclosure, consistency, coherence, evidence, structural ordering, nature and the accuracy of the examples, illustrative material, the credibility of the conclusions;
  • disadvantages, shortcomings;
  • questions and wishes to author.