Conference paper 2020

Professional Competence Management for University Students Based on Knowledge Graph Technology

HL
Huang, Lan
 SY
Sun, Yan
 YZ
Yi, Zijing
 JY
Jiang, Yongzheng
ICEIEC 2020 - Proceedings of 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication
Conference · pp. 331-335
Abstract

Effective competence management is beneficial to improve students' academic performance. Mining the hidden information in teaching data plays an important role in analyzing and modeling professional competence for students. Therefore, we propose a method for constructing a competence management model based on teaching data. By using knowledge graph technology, the model establishes a semantic network which includes seven competence entities and four kinds of relationships between them. To apply and evaluate the competence management method, the model is instantiated by using teaching data from computer science. In the part of named entity recognition, this paper uses the BiLSTM-CNN-CRF model. Compared with the BiLSTM-CRF model, the precision is improved by 1.50%, the recall rate is improved by 1.47% and the F1-score is improved by 1.48%. In the part of entity similarity calculation, in order to help students manage their competence better, the similarity of competence entities and the degree words are calculated respectively. Finally, the Neo4j graphics database is used to store and display the knowledge graph and a competence retrieval service for students is applied in this paper. © 2020 IEEE.

Keywords

Author Keywords

Machine Learning Knowledge Graph Competence management retrieval service

Index Keywords

Students University students academic performance professional competence Semantics Knowledge representation Competence management Entity similarities Hidden information Named entity recognition Semantic network
Author Affiliations
Jilin University, Changchun, Jilin, China
Funding & Acknowledgements
Grant: 20190201273JC,2020C003
This research is funded by the Education Department of Jilin Province, China (No. JJKH20200993K) , the National Natural Science Foundation of China (Nos. 61772227), the Development Project of Jilin Province of China (Nos 20190201273JC,2020C003),the Jilin Province Development and Reform Commission, China (No.2019C053-1), Guangdong Key Project for Applied Fundamental Research (Grant 2018KZDXM076).This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC).
Grant: 2018KZDXM076
This research is funded by the Education Department of Jilin Province, China (No. JJKH20200993K) , the National Natural Science Foundation of China (Nos. 61772227), the Development Project of Jilin Province of China (Nos 20190201273JC,2020C003),the Jilin Province Development and Reform Commission, China (No.2019C053-1), Guangdong Key Project for Applied Fundamental Research (Grant 2018KZDXM076).This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC).
Grant: 20180622002JC
This research is funded by the Education Department of Jilin Province, China (No. JJKH20200993K) , the National Natural Science Foundation of China (Nos. 61772227), the Development Project of Jilin Province of China (Nos 20190201273JC,2020C003),the Jilin Province Development and Reform Commission, China (No.2019C053-1), Guangdong Key Project for Applied Fundamental Research (Grant 2018KZDXM076).This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC).
Jilin Province Development and Reform Commission
Grant: No.2019C053-1
This research is funded by the Education Department of Jilin Province, China (No. JJKH20200993K) , the National Natural Science Foundation of China (Nos. 61772227), the Development Project of Jilin Province of China (Nos 20190201273JC,2020C003),the Jilin Province Development and Reform Commission, China (No.2019C053-1), Guangdong Key Project for Applied Fundamental Research (Grant 2018KZDXM076).This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC).
National Natural Science Foundation of China, NSFC
Grant: 61772227
This research is funded by the Education Department of Jilin Province, China (No. JJKH20200993K) , the National Natural Science Foundation of China (Nos. 61772227), the Development Project of Jilin Province of China (Nos 20190201273JC,2020C003),the Jilin Province Development and Reform Commission, China (No.2019C053-1), Guangdong Key Project for Applied Fundamental Research (Grant 2018KZDXM076).This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC).
Education Department of Jilin Province
Grant: JJKH20200993K
This research is funded by the Education Department of Jilin Province, China (No. JJKH20200993K) , the National Natural Science Foundation of China (Nos. 61772227), the Development Project of Jilin Province of China (Nos 20190201273JC,2020C003),the Jilin Province Development and Reform Commission, China (No.2019C053-1), Guangdong Key Project for Applied Fundamental Research (Grant 2018KZDXM076).This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC).
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