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Development of a corporate knowledge management system using machine learning techniques. / Tkachenko, Elena; Rogova, Elena; Bodrunov, Sergey; Romanov, Igor.

Proceedings of the 22nd European Conference on Knowledge Management, ECKM 2021. ed. / Alexeis Garcia-Perez; Lyndon Simkin. Academic Conferences and Publishing International Limited, 2021. p. 757-767 (Proceedings of the European Conference on Knowledge Management, ECKM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Tkachenko, E, Rogova, E, Bodrunov, S & Romanov, I 2021, Development of a corporate knowledge management system using machine learning techniques. in A Garcia-Perez & L Simkin (eds), Proceedings of the 22nd European Conference on Knowledge Management, ECKM 2021. Proceedings of the European Conference on Knowledge Management, ECKM, Academic Conferences and Publishing International Limited, pp. 757-767, 22nd European Conference on Knowledge Management, ECKM 2021, Virtual, Online, 2/09/21. https://doi.org/10.34190/EKM.21.128

APA

Tkachenko, E., Rogova, E., Bodrunov, S., & Romanov, I. (2021). Development of a corporate knowledge management system using machine learning techniques. In A. Garcia-Perez, & L. Simkin (Eds.), Proceedings of the 22nd European Conference on Knowledge Management, ECKM 2021 (pp. 757-767). (Proceedings of the European Conference on Knowledge Management, ECKM). Academic Conferences and Publishing International Limited. https://doi.org/10.34190/EKM.21.128

Vancouver

Tkachenko E, Rogova E, Bodrunov S, Romanov I. Development of a corporate knowledge management system using machine learning techniques. In Garcia-Perez A, Simkin L, editors, Proceedings of the 22nd European Conference on Knowledge Management, ECKM 2021. Academic Conferences and Publishing International Limited. 2021. p. 757-767. (Proceedings of the European Conference on Knowledge Management, ECKM). https://doi.org/10.34190/EKM.21.128

Author

Tkachenko, Elena ; Rogova, Elena ; Bodrunov, Sergey ; Romanov, Igor. / Development of a corporate knowledge management system using machine learning techniques. Proceedings of the 22nd European Conference on Knowledge Management, ECKM 2021. editor / Alexeis Garcia-Perez ; Lyndon Simkin. Academic Conferences and Publishing International Limited, 2021. pp. 757-767 (Proceedings of the European Conference on Knowledge Management, ECKM).

BibTeX

@inproceedings{efefd823a48545599f86a6950428e662,
title = "Development of a corporate knowledge management system using machine learning techniques",
abstract = "Modern knowledge management systems are often essentially data libraries that differ only in the degree of structuring and processing methodology. From the point of view of management theory, the management process consists of such stages as analysis, goal setting, planning, implementation and control. In knowledge management, as a rule, only the analytical stage is presented. The process of management itself is limited by the difficulty of forecasting the processes of development of the system of corporate and public knowledge. In our study, we attempted to solve this problem by applying machine learning techniques. The term Machine Learning was used for the first time by Arthur Lee Samuel (1959). Machine learning is a class of artificial intelligence methods, the characteristic feature of which is not a direct solution to the problem, but training in the process of applying solutions to many similar problems. The science of machine learning itself studies methods for constructing algorithms capable of learning from various inputs. As part of this study, we will be interested only in learning with a teacher, which, in turn, is divided into the following subtasks:-Classification tasks; Regression tasks; Ranking tasks; Prediction tasks. The specific feature of learning with a teacher is that there is both a lot of data in which the model searches for patterns, and answers to the forecast of the model as part of its training. We have simulated the forecasting process in relation to the knowledge system of a company operating in the securities market. Machine learning methods have shown high efficiency in solving the entire range of tasks related to knowledge management.",
keywords = "Forecasting, Knowledge management, Machine learning",
author = "Elena Tkachenko and Elena Rogova and Sergey Bodrunov and Igor Romanov",
note = "Publisher Copyright: {\textcopyright} The Authors, 2021. All Rights Reserved.; 22nd European Conference on Knowledge Management, ECKM 2021 ; Conference date: 02-09-2021 Through 03-09-2021",
year = "2021",
doi = "10.34190/EKM.21.128",
language = "English",
isbn = "9781914587061",
series = "Proceedings of the European Conference on Knowledge Management, ECKM",
publisher = "Academic Conferences and Publishing International Limited",
pages = "757--767",
editor = "Alexeis Garcia-Perez and Lyndon Simkin",
booktitle = "Proceedings of the 22nd European Conference on Knowledge Management, ECKM 2021",
address = "United Kingdom",

}

RIS

TY - GEN

T1 - Development of a corporate knowledge management system using machine learning techniques

AU - Tkachenko, Elena

AU - Rogova, Elena

AU - Bodrunov, Sergey

AU - Romanov, Igor

N1 - Publisher Copyright: © The Authors, 2021. All Rights Reserved.

PY - 2021

Y1 - 2021

N2 - Modern knowledge management systems are often essentially data libraries that differ only in the degree of structuring and processing methodology. From the point of view of management theory, the management process consists of such stages as analysis, goal setting, planning, implementation and control. In knowledge management, as a rule, only the analytical stage is presented. The process of management itself is limited by the difficulty of forecasting the processes of development of the system of corporate and public knowledge. In our study, we attempted to solve this problem by applying machine learning techniques. The term Machine Learning was used for the first time by Arthur Lee Samuel (1959). Machine learning is a class of artificial intelligence methods, the characteristic feature of which is not a direct solution to the problem, but training in the process of applying solutions to many similar problems. The science of machine learning itself studies methods for constructing algorithms capable of learning from various inputs. As part of this study, we will be interested only in learning with a teacher, which, in turn, is divided into the following subtasks:-Classification tasks; Regression tasks; Ranking tasks; Prediction tasks. The specific feature of learning with a teacher is that there is both a lot of data in which the model searches for patterns, and answers to the forecast of the model as part of its training. We have simulated the forecasting process in relation to the knowledge system of a company operating in the securities market. Machine learning methods have shown high efficiency in solving the entire range of tasks related to knowledge management.

AB - Modern knowledge management systems are often essentially data libraries that differ only in the degree of structuring and processing methodology. From the point of view of management theory, the management process consists of such stages as analysis, goal setting, planning, implementation and control. In knowledge management, as a rule, only the analytical stage is presented. The process of management itself is limited by the difficulty of forecasting the processes of development of the system of corporate and public knowledge. In our study, we attempted to solve this problem by applying machine learning techniques. The term Machine Learning was used for the first time by Arthur Lee Samuel (1959). Machine learning is a class of artificial intelligence methods, the characteristic feature of which is not a direct solution to the problem, but training in the process of applying solutions to many similar problems. The science of machine learning itself studies methods for constructing algorithms capable of learning from various inputs. As part of this study, we will be interested only in learning with a teacher, which, in turn, is divided into the following subtasks:-Classification tasks; Regression tasks; Ranking tasks; Prediction tasks. The specific feature of learning with a teacher is that there is both a lot of data in which the model searches for patterns, and answers to the forecast of the model as part of its training. We have simulated the forecasting process in relation to the knowledge system of a company operating in the securities market. Machine learning methods have shown high efficiency in solving the entire range of tasks related to knowledge management.

KW - Forecasting

KW - Knowledge management

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85117139957&partnerID=8YFLogxK

U2 - 10.34190/EKM.21.128

DO - 10.34190/EKM.21.128

M3 - Conference contribution

AN - SCOPUS:85117139957

SN - 9781914587061

T3 - Proceedings of the European Conference on Knowledge Management, ECKM

SP - 757

EP - 767

BT - Proceedings of the 22nd European Conference on Knowledge Management, ECKM 2021

A2 - Garcia-Perez, Alexeis

A2 - Simkin, Lyndon

PB - Academic Conferences and Publishing International Limited

T2 - 22nd European Conference on Knowledge Management, ECKM 2021

Y2 - 2 September 2021 through 3 September 2021

ER -

ID: 99665694