IEEE Systems, Man and Cybernetics Magazine - April 2022 - 38

representation, fusion, and reasoning of heterogeneous
knowledge graphs, which are generalized and practical to
high-order knowledge graphs. What's more, the tensorbased
knowledge fusion and reasoning framework and
approaches proposed in this article can be applied to a
wide range of real-world scenarios, such as semantic
search, knowledge question and answer recommendation
systems, and so on.
Furthermore, we want to explain that this article provides
tensor-based ideas and frameworks for knowledge
reasoning and fusion. Tensors have sufficient mathematical
theories and adaptable operations, from which we
will further explore knowledge fusion and reasoning
methods based on tensors in future work.
Acknowledgment
Jing Yang is the corresponding author of this article.
About the Authors
Jing Yang (jing_yang@hust.edu.cn) earned her B.E.
degree in communication engineering from Harbin University
of Science and Technology. She is a Ph.D. degree
student at Huazhong University of Science and Technology,
Wuhan, 430074, China. Her research interests
include knowledge graph, representation learning, and
data mining.
Laurence T. Yang (ltyang@ieee.org) earned her B.E.
degree in computer science and technology and B.Sc.
degree in applied physics both from Tsinghua University,
and her Ph.D. degree in computer science from the University
of Victoria. He is a professor at Huazhong University of
Science and Technology, Wuhan, 430074, China, and at
St. Francis Xavier University, Antigonish, NSB2G 2W5, Canada.
His research interests include parallel and distributed
computing, embedded and ubiquitous/pervasive computing,
and big data. He is a Fellow of IEEE.
Yuan Gao (yuang@hust.edu.cn) earned his B.E. degree
from Wuhan University of Science and Technology. He is
currently studying for his Ph.D. degree in the School of
Computer Science and Technology, Huazhong University
of Science and Technology, Wuhan, 430074, China. His
research interests include data mining, parallel and distributed
computing, security computing, and deep learning.
Huazhong Liu (sharpshark_ding@163.com) earned his
B.Sc. degree in computer science from Jiangxi Normal University,
his M.Sc. degree in computer science from Hunan
Normal University, and his Ph.D. degree in computer science
from Huazhong University of Science and Technology.
He is a professor at Hainan University, Haikou, 570000,
China. His research interests include big data, cloud computing,
Internet of Things, and scheduling optimization. His
research has been supported by the National Natural Science
Foundation of China.
Hao Wang (wanghao1999_cs@163.com) earned his
B.E. degree from the Central University of Finance and
38 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE April 2022
Economics. He is an M.Sc. degree student in computer
science and technology at Huazhong University of
Science and Technology, Wuhan, 430074, China. His
research interests include knowledge graph and natural
language processing.
Xia Xie (shelicy@qq.com) earned her Ph.D. degree from
Huazhong University of Science and Technology in 2006.
She is a professor at Hainan University, Haikou, 570000,
China. Her research interests include knowledge graph, data
mining, and big data.
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