Knowledge graph construction and complementation for research projects

doi:10.23919/JSEE.2025.000064

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (3): 725-735.doi: 10.23919/JSEE.2025.000064

• SYSTEMS ENGINEERING • Previous Articles

Knowledge graph construction and complementation for research projects

Tongxin LI(), Mu LIN(), Weiping WANG(), Xiaobo LI(), Tao WANG()

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received:2023-04-07 Online:2025-06-18 Published:2025-07-10
Contact: Tao WANG E-mail:litongxin@nudt.edu.cn;linmu023@163.com;wangwp@nudt.edu.cn;lixiaobo@nudt.edu.cn;wangtao1976@nudt.edu.cn
About author:
LI Tongxin was born in 1995. She received her M.S. degree in applied statistics from the College of Science, Harbin Institute of Technology, in 2019. She is pursuing her Ph.D. degree at National University of Defense Technology. Her research interests include systems engineering and simulation, knowledge graph represents learning and natural language processing. E-mail: litongxin@nudt.edu.cn

LIN Mu was born in 1983. He received his M.S. degree from the College of Systems Engineering, National University of Defense Technology, Changsha, China, in 2006. He is pursuing his Ph.D. degree at National University of Defense Technology. His research interests include systems engineering and simulation, machine learning, and data mining. E-mail: linmu023@163.com

WANG Weiping was born in 1963. He received his Ph.D. degree in systems engineering from the National University of Defense Technology, Changsha, China in 1997. He is a professor at National University of Defense Technology. His research interests focus on intelligent decision experimentation, multi-modal knowledge extraction and knowledge integrated computation. E-mail: wangwp@nudt.edu.cn

LI Xiaobo was born in 1983. He received his Ph.D. degree from the College of Systems Engineering, National University of Defense Technology, Changsha, China in 2013. He is currently an associate professor at National University of Defense Technology. His main research interests include system of systems engineering and simulation. E-mail: lixiaobo@nudt.edu.cn

WANG Tao was born in 1976. He received his Ph.D. degree in computer science from the National University of Defense Technology in 2007. He is a professor at National University of Defense Technology. His research interests include artificial intelligence and unmanned aerial vehicle (UAV) swarm. E-mail: wangtao1976@nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (72101263).

Abstract

Abstract:

Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies. However, the data from these projects is often complex and inadequate, making it challenging for researchers to conduct in-depth data mining to improve policies or management. To address this problem, this paper adopts a top-down approach to construct a knowledge graph (KG) for research projects. Firstly, we construct an integrated ontology by referring to the metamodel of various architectures, which is called the meta-model integration conceptual reference model. Subsequently, we use the dependency parsing method to extract knowledge from unstructured textual data and use the entity alignment method based on weakly supervised learning to classify the extracted entities, completing the construction of the KG for the research projects. In addition, a knowledge inference model based on representation learning is employed to achieve knowledge completion and improve the KG. Finally, experiments are conducted on the KG for research projects and the results demonstrate the effectiveness of the proposed method in enriching incomplete data within the KG.

Key words: research projects, knowledge graph (KG), KG completion

Tongxin LI, Mu LIN, Weiping WANG, Xiaobo LI, Tao WANG. Knowledge graph construction and complementation for research projects[J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 725-735.

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Metrix	WMR	WH@2/%	WH@10/%	WH@20/%
TransE	125.22	5.22	16.79	28.34
DistMult	313.41	3.66	6.44	8.53
UKGE_logi	168.40	4.77	18.14	25.28
UKGE_rect	274.24	3.01	9.25	11.81
TA-TransE	164.79	2.27	15.88	25.97
TA-DistMult	370.92	0.62	1.85	4.69
TRCEM_logi	115.24	4.88	20.26	33.96
TRCEM_rect	111.88	5.36	24.79	41.68

Metrix	MSE	MAE
UKGE_logi	13.92	28.98
UKGE_rect	14.32	26.19
TRCEM_logi	11.07	27.60
TRCEM_rect	10.73	21.76

Metrix	WMR	WH@2/%	WH@10/%	WH@20/%	MSE	MAE
TRCEM_logi	115.24	4.88	20.26	33.96	0.110 7	0.276 0
TRCEM_t-	119.28	4.05	19.75	30.97	0.131 8	0.306 3
TRCEM_s-	212.32	3.73	14.36	19.64	0.130 5	0.292 9

Knowledge graph construction and complementation for research projects

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