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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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

[1]	Xi, X. (2021). Try to Explore New Ways of Discipline Inspection with “Internet Plus” Thinking, Party School of the Communist Party of China Heilongjiang Provincial Committee.
[2]	Li, S., Liu, B., Ye, L., Zhang, H. & Fang, B. (2019), Element-Aware Legal Judgment Prediction for Criminal Cases with Confusing Charges, 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 660-667, doi: 10.1109/ICTAI.2019.00097.
[3]	Wang, Y., Gao, J., & Chen, J. (2020). Deep Learning Algorithm for Judicial Judgment Prediction Based on BERT, 2020 5th International Conference on Computing, Communication and Security (ICCCS), 1-6, doi: 10.1109/ICCCS49678.2020.9277068.
[4]	Wang, P. (2021). Research on the Construction of Knowledge Database of Discipline Inspection and Supervision Laws and Regulations and Automatic Response System Based on Knowledge Graph, Inner Mongolia Agricultural University.
[5]	Liu, Y., et al., (2021). Construction of Knowledge Graph Based on Discipline Inspection and Supervision, 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (Trust Com), 1467-1472, doi: 10.1109/TrustCom53373.2021.00209.
[6]	Jacob, D., Ming-Wei C., Kenton L., & Kristina T. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186, arXiv Preprint arXiv: 1810.04805.
[7]	Yu, T., Jin, R., Han, X., et al. (2020). Review of pre-training models for natural language processing. Computer Engineering and Applications, 56 (23), 12-22. doi: 10.3778/j.issn.1002-8331.2006-0040.
[8]	Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. G., Łukasz, K., & Illia, P., (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 6000–6010.
[9]	Huang, Z., Xu, W., Yu, K. (2015). Bidirectional LSTM-CRF Models for Sequence Tagging. https://doi.org/10.48550/arXiv.1508.01991.
[10]	Ju, P., Zhang, W., Ning, J., et al. (2011). Geospatial Named Entities Recognition Using Combination of CRF and Rules. Computer Engineering, 37 (7): 210–212, 215. doi: 10.3969/j.issn.1000-3428.2011.07.071.
[11]	Liu, Q., Li, Y., Duan, H., Liu, Y. & Qin, Z. (2016). Knowledge Graph Construction Techniques: Taxonomy, Survey and Future Directions, Journal of Computer Research and Development, 53 (03), 582-600. doi: 10.7544/issn1000-1239.2016.20148228.
[12]	Hang, T., Feng, J., & Lu, J. (2021). Knowledge Graph Construction Techniques: Taxonomy, Survey and Future Directions. Computer Science, 48 (02), 175-189.
[13]	Lei, Y., Li, J., Zeng, Y., Wu, J., & Wang, X. (2020). Construction of Military Knowledge Map Based on Multi-source Data Fusion. presented at The 8th China Conference on Command and Contro, 23: 2567–2592, https://doi.org/10.1007/s11280-020-00811-0
[14]	Liu, W., Xu, T., Xu, Q., Song, J., & Zu, Y. (2019). An Encoding Strategy Based Word-Character LSTM for Chinese NER. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 2379–2389, doi: 10.18653/v1/N19-1247.
[15]	L. Cheng, M., Yu, H., Feng, Y., et al. (2020). Fishery standard named entity recognition with integrated attention mechanism and BiLSTM+CRF. Journal of Dalian Ocean University, 35 (2): 296-301.
[16]	Souza, F., Nogueira, R., & Lotufo, R. (2019). Portuguese Named Entity Recognition using BERT-CRF, arXiv preprint arXiv: 1909.10649.
[17]	Li, N., Guan, H., Yang, P., Dong, W. (2020). BERT-IDCNN-CRF for named entity recognition in Chinese. Journal of Shandong University (natural science), 2020, 55 (1): 102-109. doi: 10.6040/j.issn.1671-9352.2.2019.076.
[18]	KIM, Y. (2014). Convolutional neural networks for sentence classification. arXiv Preprint arXiv: 1408.5882.
[19]	Wang, Y., (2021). Research on Text Classification in Thangka Domain Based on Bert. Modern Computer, 27 (33), 99-103 doi: 10.3969/j.issn.1007-1423.2021.33.018.
[20]	Liu, H., Wang, L., Chen, Y. et al. (2020). A Method for Case Factor Recognition Based on Pre-trained Language Models. In Proceedings of the 19th Chinese National Conference on Computational Linguistics, 743–753, Haikou, China. Chinese Information Processing Society of China. https://aclanthology.org/2020.ccl-1.69

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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