Citation: Liang, K.; Zhou, B.; Zhang,
Y.; He, Y.; Guo, X.; Zhang, B. A
Multi-Entity Knowledge Joint
Extraction Method of
Communication Equipment Faults
for Industrial IoT. Electronics 2022, 11,
979. https://doi.org/10.3390/
electronics11070979
Academic Editor: Sławomir
Nowaczyk
Received: 22 February 2022
Accepted: 20 March 2022
Published: 22 March 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
A Multi-Entity Knowledge Joint Extraction Method of
Communication Equipment Faults for Industrial IoT
Kun Liang
1
, Baoxian Zhou
1,
* , Yiying Zhang
1
, Yeshen He
2
, Xiaoyan Guo
3
and Bo Zhang
4
1
College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China;
liangkun@tust.edu.cn (K.L.); yiyingzhang@tust.edu.cn (Y.Z.)
2
China Gridcom Co., Ltd., Shenzhen 518109, China; heyeshen@sgitg.sgcc.com.cn
3
Information and Communication Company, State Grid Tianjin Electric Power Company,
Tianjin 300140, China; xiaoyan.guo@tj.sgcc.com.cn
4
State Grid Smart Grid Research Institute Co., Ltd., Nanjing 210003, China; zhangbo@geiri.sgcc.com.cn
* Correspondence: zhoubaoxian@yeah.net
Abstract:
The Industrial Internet of Things (IIoT) deploys massive communication devices for
information collection and process control. Once it reaches failure, it will seriously affect the operation
of the industrial system. This paper proposes a new method for multi-entity knowledge joint
extraction (MEKJE) of IIoT communication equipment faults. This method constructs a multi-task
tightly coupled model of fault entity and relationship extraction. We use it to implement word
embedding and bidirectional semantic capture to generate computable text vectors. At the same
time, a multi-entity segmentation method is proposed, which uses noise filtering to distinguish the
multi-fault relationship of single corpus. We constructed a dataset of communication failures in
power IIoT and conducted experiments. The experimental results show that the method performs
best in tests with the Faulty Text dataset and the CLUENER dataset. In particular, the model achieves
an F1 value of 78.6% in the evaluation of relationship extraction for multiple entities, and a significant
improvement of 5–8% in its accuracy and recall. It enables effective mapping and accurate extraction
of fault knowledge data.
Keywords:
knowledge graph; entity recognition; relationship extraction; joint learning; multi-entity
segmentation
1. Introduction
The industrial internet of things (IIoT) deploys a large number of perception and
communication devices [
1
], which realize the real-time state perception of physical space–
information space interaction and the normal operation of the system. Once a fault occurs,
it is easy to cause problems, such as energy resource scheduling, distribution and out
of control transmission. It will seriously endanger the operation safety of the integrated
energy system [
2
]. Therefore, it is of great significance to accurately extract equipment fault
information, locate the entity relationship of fault equipment, construct the fault knowledge
graph of IIoT communication equipment [
3
,
4
] and realize real-time fault analysis and
efficient troubleshooting of IIoT communication equipment.
Named entity recognition and relationship extraction are two key technologies to real-
ize knowledge extraction [
5
]. Named entity recognition techniques are divided into three
main approaches in terms of implementation techniques: rule-based, probabilistic graph
and deep learning [
6
]. Rule-based learning relies more on manual or domain dictionaries,
which not only lack flexibility, but also has poor recognition efficiency. With the develop-
ment of probabilistic graphs, the learning of entity sequence information is carried out
through directed and undirected graphs [
7
]. It reduces manual involvement and provides
some improvement in efficiency, but generalization capability is poor and the efficiency
of entity recognition needs to be improved [
8
]. Deep learning techniques build multiple
Electronics 2022, 11, 979. https://doi.org/10.3390/electronics11070979 https://www.mdpi.com/journal/electronics
