Proceedings of the 2019 International Conference on Management, Education Technology and Economics (ICMETE 2019)

A Study for Smart Vocational Skill Training Based on Knowledge Graph

Authors
Yanhua Wang, Yong Li
Corresponding Author
Yong Li
Available Online May 2019.
DOI
10.2991/icmete-19.2019.55How to use a DOI?
Keywords
Vocational skill training; Knowledge graph; Ant colony algorithm; Python
Abstract

In view of the low efficiency, optimization difficulties and uncertain training effect in the process of vocational skill training, this paper studies smart vocational training based on knowledge graph. According to the needs of vocational skills training, the system creates knowledge graph objects by natural language processing automatically, then utilizes ant colony algorithm to discover the path of knowledge graph, and finally develops the training plan. The vocational skill training is designed and developed in Python language.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2019 International Conference on Management, Education Technology and Economics (ICMETE 2019)
Series
Advances in Economics, Business and Management Research
Publication Date
May 2019
ISBN
10.2991/icmete-19.2019.55
ISSN
2352-5428
DOI
10.2991/icmete-19.2019.55How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yanhua Wang
AU  - Yong Li
PY  - 2019/05
DA  - 2019/05
TI  - A Study for Smart Vocational Skill Training Based on Knowledge Graph
BT  - Proceedings of the 2019 International Conference on Management, Education Technology and Economics (ICMETE 2019)
PB  - Atlantis Press
SP  - 229
EP  - 232
SN  - 2352-5428
UR  - https://doi.org/10.2991/icmete-19.2019.55
DO  - 10.2991/icmete-19.2019.55
ID  - Wang2019/05
ER  -