Development of a prototype of an informational and analytical Internet resource on machine learning
https://doi.org/10.25205/1818-7900-2025-23-3-44-56
Abstract
With the rapid development of artificial intelligence and machine learning technologies, access to structured and understandable information in the field is becoming critical for both specialists and a wide range of users. Existing resources often suffer from data fragmentation, lack of systematization, and difficulty in navigation. This paper is devoted to the development of a prototype Internet resource [1] that would solve these problems and provide users with access to an extensive knowledge base on machine learning, including descriptions of algorithms, methods, tools, and examples of their application.
About the Author
I. O. Plotnikova
Novosibirsk State University
Russian Federation
Russian Federation
Irina O. Plotnikova, Master Student
Novosibirsk
References
1. Zagorulko Yu. A., Zagorulko G. B., Borovikova O. I. Technology for building subject based intelligent scientific internet resources based on ontology. Software Engineering, 2016, vol. 7, no. 2, рр. 51–60. (in Russ.)
2. Shumskiy S. A. Mashinnyy intellekt. Ocherki po teorii mashinnogo obucheniya i iskusstvennogo intellekta [Machine Intelligence. Essays on the Theory of Machine Learning and Artificial Intelligence]. Moscow, RIOR, 2019, 340 р. (in Russ.)
3. Zagorulko Yu. A. Obzor sovremennykh sredstv formalizatsii semantiki oblastey znaniy na osnove ontologiy [Modern means for formalizing the semantics of knowledge areas on the basis of ontologies]. Informatsionnye i matematicheskiye tekhnologii v nauke i upravlenii [Information and Mathematical Technologies in Science and Management], 2018, no. 3 (11), pр. 27–36. DOI: 10.25729/2413-0133-2018-3-03 (in Russ.)
4. Lukashevich N. V., Dobrov B. V. Proyektirovaniye lingvisticheskikh ontologiy dlya informatsionnykh sistem v shirokikh predmetnykh oblastyakh [Developing linguistic ontologies in broad domains]. Ontologiya proyektirovaniya [Ontology of Design], 2015, no. 1 (15), pр. 47–69. (in Russ.)
5. Gavrilova T. A., Horoshevskij V. F. Bazy znaniy intellektualnykh sistem. Uchebnik [Knowledge Bases of Intelligent Systems. Textbook]. St. Petersburg, Piter, 2000, 384 p. (in Russ.)
6. Baader F., Calvanese D., McGuinness D. L., Nardi D., Patel-Schneider P. F. The Description Logic Handbook: Theory, Implementation, Applications. Cambridge, 2003. 574 р.
7. Antoniou G., Harmelen F. Web Ontology Language: OWL. Handbook on Ontologies. Berlin: Springer Verlag, 2004. pp. 67–92.
8. Protégé. A free, open-source ontology editor and framework for building intelligent systems. URL: http://protege.stanford.edu/
9. Sighn A. Machine Learning with Python, 2019, рр. 7–10.
10. Izenman A. J. Modern Multivariate Statistical Techniques, Springer Texts in Statistics. Springer Science+Business Media New York, 2013.
11. Breiman L. Bagging predictors. Machine Learning, 1996, 24, рр. 123–140.
12. Freund Y., Schapire R. E. A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence, 1999, no. 14 (5), рр. 771–780.
13. Wolpert D. H. Stacked Generalization. Neural Networks, 1992, no. 5, рр. 241–259.
14. Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press, 2016.
15. Mitchell T. M. Machine Learning. McGraw-Hill Education, 1997.
16. Flakh P. Mashinnoye obucheniye. Nauka i iskusstvo postroyeniya algoritmov, kotoryye izvlekayut znaniya iz dannykh [Machine learning. The science and art of building algorithms that extract knowledge from data]. Moscow, DMK Press, 2023. 401 p. (in Russ.)
Review
For citations:
Plotnikova I.O. Development of a prototype of an informational and analytical Internet resource on machine learning. Vestnik NSU. Series: Information Technologies. 2025;23(3):44-56. (In Russ.) https://doi.org/10.25205/1818-7900-2025-23-3-44-56
Views:
10
Email this article 