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Article

A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT

by
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
2
China Gridcom Co., Ltd., Shenzhen 518109, China
3
Information and Communication Company, State Grid Tianjin Electric Power Company, Tianjin 300140, China
4
State Grid Smart Grid Research Institute Co., Ltd., Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(7), 979; https://doi.org/10.3390/electronics11070979
Submission received: 22 February 2022 / Revised: 20 March 2022 / Accepted: 20 March 2022 / Published: 22 March 2022
(This article belongs to the Topic Data Science and Knowledge Discovery)
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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 knowledge graph; entity recognition; relationship extraction; joint learning; multi-entity segmentation

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MDPI and ACS Style

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

AMA Style

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(7):979. https://doi.org/10.3390/electronics11070979

Chicago/Turabian Style

Liang, Kun, Baoxian Zhou, Yiying Zhang, Yeshen He, Xiaoyan Guo, and Bo Zhang. 2022. "A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT" Electronics 11, no. 7: 979. https://doi.org/10.3390/electronics11070979

APA Style

Liang, K., Zhou, B., Zhang, Y., He, Y., Guo, X., & Zhang, B. (2022). A Multi-Entity Knowledge Joint Extraction Method of Communication Equipment Faults for Industrial IoT. Electronics, 11(7), 979. https://doi.org/10.3390/electronics11070979

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