Irkutsk region, Russian Federation
Irkutsk region, Russian Federation
GRNTI 28.29 Системный анализ
OKSO 03.03.02 Прикладная математика и информатика
BBK 221 Математика
TBK 55 Энергетика. Промышленность
The article discusses the use of methods of semantic analysis and natural language processing to support research and forecasting the innovative development of the energy infrastructure of the Russian Federation. The existing methods and approaches to the organization of monitoring of technological solutions and innovative scientific developments are considered. To automate monitoring, the authors propose the use of natural language processing (NLP) methods. Semantic analysis and knowledge integration are based on a system of ontologies. The paper presents the main methods and approaches to building an infrastructure for processing open Big Data. Application of the proposed methods makes it possible to improve the quality of scientific research in this area and make them better.
scientific and technological forecasting, semantic analysis, natural language processing, Big data, scientific research support
1. C. Cagnin, M. Keenan, R. Johnston, F. Scapolo, R. Barre. Future-oriented technology analysis. strategic intelligence for an innovative economy. – Springer, 2008. – 170 p., DOI: 10.1007/978-3-540-68811-2.
2. Woon, W.L., Zeineldin, H., Madnick, S. Bibliometric analysis of distributed generation // Technol. Forecast. Soc. Change, vol. 78(3), 2011. – pp. 408–420. – http://hdl.handle.net/1721.1/98911
3. Prognoz nauchno-tehnologicheskogo razvitiya otrasley toplivno-energeticheskogo kompleksa Rossii na period do 2035 goda. Ministrestvo energetiki Rossiyskoy federacii, 2016. –http://minenergo.gov.ru/system/download-pdf/6365/66647
4. V.V. Dikovickiy, M.G. Shishaev. Obrabotka tekstov estestvennogo yazyka v modelyah poiskovyh sistem / Trudy Kol'skogo nauchnogo centra RAN, 2010, №3. – S. 29-34.
5. J.B. Lovins. Development of a Stemming Algorithm // Mechanical Translation and Computational Linguistics. – 1968. – T. 11.
6. Jones K.S. A statistical interpretation of term specificity and its application in retrieval / Journal of Documentation: zhurnal. – MCB University: MCB University Press, 2004. – Vol. 60, no. 5. – P. 493-502. – ISSN 0022-0418.
7. Mikolov T., Chen K., Corrado G., Dean J. Efficient Estimation of Word Representations in Vector Space // Proceedings of Workshop at ICLR. – 2013a.
8. Mikolov T., Yih W.-T., Zweig G. Linguistic Regularities in Continuous Space Word Representations // Proceedings of NAACL HLT. – 2013b.
9. S. Bird, E. Klein, E. Loper. Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit. – http://www.nltk.org/book/
10. T.U. Daim, D. Chiavetta, A.L. Porter, O. Saritas. Anticipating future innovation pathways through large data analysis // Innovation, technology, and knowledge management. – Springer, 2016, – 360 p., DOI 10.1007/978-3-319-39056-7.
11. A. Kopaygorodsky. Methods and technologies of building an intelligent service for energy technology forecasting // International Scientific Journal Industry 4.0, 2018, Issue 5. –Scientific Technical Union of Mechanical Engineering "Industry 4.0". – Pp. 223-228. ISSN: 2534-8582
12. A. Kopaygorodsky. Technology of Application of Software Tools for Energy Technology Forecasting // Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019). Part of series Atlantis Highlights in Computer Sciences, vol. 3. 2019. – Atlantis Press. – Pp. 267-272. ISBN 978-94-6252-868-0, DOI: 10.2991/csit-19.2019.47.
