Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph

Yue Wang; Yuefeng Liu; Hanyu Zhang; HaoFeng Liu; Xiang Bao; Bo Liu; Jianmin Dong

doi:doi:10.11648/j.ajece.20220601.14

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Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph

Yue Wang, Yuefeng Liu, Hanyu Zhang, HaoFeng Liu, Xiang Bao, Bo Liu, Jianmin Dong

Published in American Journal of Electrical and Computer Engineering (Volume 6, Issue 1)

Received: 28 April 2022 Accepted: 18 May 2022 Published: 26 May 2022

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Abstract

As an important link to realize intelligent discipline inspection and supervision, the term “case characterization and discipline measurement” refers to the automatic extraction of material facts from case description and the conclusion of conformity and nonconformity after comparison in accordance with legal norms. In response to the problem that there was no special method for the task of case characterization and discipline measurement, the paper combined the practical case handling process of the staff and proposed a method of case characterization and discipline measurement based on discipline inspection and supervision knowledge graph. The method uses the knowledge graph as auxiliary information and aligns the entities of regulations and cases using knowledge fusion technology to construct the discipline inspection and supervision knowledge graph. For the newborn case descriptions, named entity recognition technology is used to extract the key elements that determine the verdict outcome. Similar cases were identified with the same discipline breach nature. Then, text classification technology is used to predict the severity of case circumstances. Combined with the disciplinary violation facts, the disciplinary result is given according to the party discipline rules. Experiments were carried out with a dataset of typical cases notified by the discipline inspection and supervision. According to the experimental results, the proposed method shows its validity, which improves the interpretability of case characterization and discipline measurement and fills the field gap.

Published in	American Journal of Electrical and Computer Engineering (Volume 6, Issue 1)
DOI	10.11648/j.ajece.20220601.14
Page(s)	30-39
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Case Characterization and Discipline Measurement, Knowledge Graph, Natural Language Processing

References

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APA Style

Yue Wang, Yuefeng Liu, Hanyu Zhang, HaoFeng Liu, Xiang Bao, et al. (2022). Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph. American Journal of Electrical and Computer Engineering, 6(1), 30-39. https://doi.org/10.11648/j.ajece.20220601.14

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ACS Style

Yue Wang; Yuefeng Liu; Hanyu Zhang; HaoFeng Liu; Xiang Bao, et al. Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph. Am. J. Electr. Comput. Eng. 2022, 6(1), 30-39. doi: 10.11648/j.ajece.20220601.14

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AMA Style

Yue Wang, Yuefeng Liu, Hanyu Zhang, HaoFeng Liu, Xiang Bao, et al. Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph. Am J Electr Comput Eng. 2022;6(1):30-39. doi: 10.11648/j.ajece.20220601.14

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@article{10.11648/j.ajece.20220601.14,
  author = {Yue Wang and Yuefeng Liu and Hanyu Zhang and HaoFeng Liu and Xiang Bao and Bo Liu and Jianmin Dong},
  title = {Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {6},
  number = {1},
  pages = {30-39},
  doi = {10.11648/j.ajece.20220601.14},
  url = {https://doi.org/10.11648/j.ajece.20220601.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20220601.14},
  abstract = {As an important link to realize intelligent discipline inspection and supervision, the term “case characterization and discipline measurement” refers to the automatic extraction of material facts from case description and the conclusion of conformity and nonconformity after comparison in accordance with legal norms. In response to the problem that there was no special method for the task of case characterization and discipline measurement, the paper combined the practical case handling process of the staff and proposed a method of case characterization and discipline measurement based on discipline inspection and supervision knowledge graph. The method uses the knowledge graph as auxiliary information and aligns the entities of regulations and cases using knowledge fusion technology to construct the discipline inspection and supervision knowledge graph. For the newborn case descriptions, named entity recognition technology is used to extract the key elements that determine the verdict outcome. Similar cases were identified with the same discipline breach nature. Then, text classification technology is used to predict the severity of case circumstances. Combined with the disciplinary violation facts, the disciplinary result is given according to the party discipline rules. Experiments were carried out with a dataset of typical cases notified by the discipline inspection and supervision. According to the experimental results, the proposed method shows its validity, which improves the interpretability of case characterization and discipline measurement and fills the field gap.},
 year = {2022}
}

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TY  - JOUR
T1  - Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph
AU  - Yue Wang
AU  - Yuefeng Liu
AU  - Hanyu Zhang
AU  - HaoFeng Liu
AU  - Xiang Bao
AU  - Bo Liu
AU  - Jianmin Dong
Y1  - 2022/05/26
PY  - 2022
N1  - https://doi.org/10.11648/j.ajece.20220601.14
DO  - 10.11648/j.ajece.20220601.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 30
EP  - 39
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20220601.14
AB  - As an important link to realize intelligent discipline inspection and supervision, the term “case characterization and discipline measurement” refers to the automatic extraction of material facts from case description and the conclusion of conformity and nonconformity after comparison in accordance with legal norms. In response to the problem that there was no special method for the task of case characterization and discipline measurement, the paper combined the practical case handling process of the staff and proposed a method of case characterization and discipline measurement based on discipline inspection and supervision knowledge graph. The method uses the knowledge graph as auxiliary information and aligns the entities of regulations and cases using knowledge fusion technology to construct the discipline inspection and supervision knowledge graph. For the newborn case descriptions, named entity recognition technology is used to extract the key elements that determine the verdict outcome. Similar cases were identified with the same discipline breach nature. Then, text classification technology is used to predict the severity of case circumstances. Combined with the disciplinary violation facts, the disciplinary result is given according to the party discipline rules. Experiments were carried out with a dataset of typical cases notified by the discipline inspection and supervision. According to the experimental results, the proposed method shows its validity, which improves the interpretability of case characterization and discipline measurement and fills the field gap.
VL  - 6
IS  - 1
ER  -

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Author Information

Yue Wang

School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China
Yuefeng Liu

School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China
Hanyu Zhang

School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China
HaoFeng Liu

School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China
Xiang Bao

School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China
Bo Liu

School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China
Jianmin Dong

Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory, Hohhot, China

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APA Style

Yue Wang, Yuefeng Liu, Hanyu Zhang, HaoFeng Liu, Xiang Bao, et al. (2022). Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph. American Journal of Electrical and Computer Engineering, 6(1), 30-39. https://doi.org/10.11648/j.ajece.20220601.14

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ACS Style

Yue Wang; Yuefeng Liu; Hanyu Zhang; HaoFeng Liu; Xiang Bao, et al. Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph. Am. J. Electr. Comput. Eng. 2022, 6(1), 30-39. doi: 10.11648/j.ajece.20220601.14

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AMA Style

Yue Wang, Yuefeng Liu, Hanyu Zhang, HaoFeng Liu, Xiang Bao, et al. Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph. Am J Electr Comput Eng. 2022;6(1):30-39. doi: 10.11648/j.ajece.20220601.14

Copy | Download

@article{10.11648/j.ajece.20220601.14,
  author = {Yue Wang and Yuefeng Liu and Hanyu Zhang and HaoFeng Liu and Xiang Bao and Bo Liu and Jianmin Dong},
  title = {Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {6},
  number = {1},
  pages = {30-39},
  doi = {10.11648/j.ajece.20220601.14},
  url = {https://doi.org/10.11648/j.ajece.20220601.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20220601.14},
  abstract = {As an important link to realize intelligent discipline inspection and supervision, the term “case characterization and discipline measurement” refers to the automatic extraction of material facts from case description and the conclusion of conformity and nonconformity after comparison in accordance with legal norms. In response to the problem that there was no special method for the task of case characterization and discipline measurement, the paper combined the practical case handling process of the staff and proposed a method of case characterization and discipline measurement based on discipline inspection and supervision knowledge graph. The method uses the knowledge graph as auxiliary information and aligns the entities of regulations and cases using knowledge fusion technology to construct the discipline inspection and supervision knowledge graph. For the newborn case descriptions, named entity recognition technology is used to extract the key elements that determine the verdict outcome. Similar cases were identified with the same discipline breach nature. Then, text classification technology is used to predict the severity of case circumstances. Combined with the disciplinary violation facts, the disciplinary result is given according to the party discipline rules. Experiments were carried out with a dataset of typical cases notified by the discipline inspection and supervision. According to the experimental results, the proposed method shows its validity, which improves the interpretability of case characterization and discipline measurement and fills the field gap.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph
AU  - Yue Wang
AU  - Yuefeng Liu
AU  - Hanyu Zhang
AU  - HaoFeng Liu
AU  - Xiang Bao
AU  - Bo Liu
AU  - Jianmin Dong
Y1  - 2022/05/26
PY  - 2022
N1  - https://doi.org/10.11648/j.ajece.20220601.14
DO  - 10.11648/j.ajece.20220601.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 30
EP  - 39
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20220601.14
AB  - As an important link to realize intelligent discipline inspection and supervision, the term “case characterization and discipline measurement” refers to the automatic extraction of material facts from case description and the conclusion of conformity and nonconformity after comparison in accordance with legal norms. In response to the problem that there was no special method for the task of case characterization and discipline measurement, the paper combined the practical case handling process of the staff and proposed a method of case characterization and discipline measurement based on discipline inspection and supervision knowledge graph. The method uses the knowledge graph as auxiliary information and aligns the entities of regulations and cases using knowledge fusion technology to construct the discipline inspection and supervision knowledge graph. For the newborn case descriptions, named entity recognition technology is used to extract the key elements that determine the verdict outcome. Similar cases were identified with the same discipline breach nature. Then, text classification technology is used to predict the severity of case circumstances. Combined with the disciplinary violation facts, the disciplinary result is given according to the party discipline rules. Experiments were carried out with a dataset of typical cases notified by the discipline inspection and supervision. According to the experimental results, the proposed method shows its validity, which improves the interpretability of case characterization and discipline measurement and fills the field gap.
VL  - 6
IS  - 1
ER  -

Copy | Download