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

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), 2022. Published by Science Publishing Group

Keywords

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

References
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Cite This Article
  • 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
  • School of Information Engineering, Inner Mongolia University of Science & Technology, Baotou, China

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

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

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

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

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

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

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