Skip to main content
Log in

Establishment Method of Knowledge Graphs for Public Security Cases

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

With the continuous improvement of the informatization level of the police, how to make accurate intelligence analysis based on the modeling of massive multimodal data has always been a real intricate problem during the applications of public security big data. Knowledge graph technique can fuse and marge multimodal data in public security business to different intelligence entity elements including person, thing, material, time, and position and reconstruct the deep relations among data in the digital space, which can provide computable and minable data resources with logical hierarchy for intelligence analysis. This study started from the case text data in public security domain, attempted ontology modeling for knowledge mapping in public security domain and explored the establishment mode and application direction of knowledge graphs in public security domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Li, Q., Li, F., Li, S., Li, X., Liu, K., Liu, Q., and Dong, P., Improving entity linking by introducing knowledge graph structure information, Appl. Sci., 2022, vol. 12, no. 5, p. 2702. https://doi.org/10.3390/app12052702

    Article  Google Scholar 

  2. Shan, L., Kun, Z., and Jun-ru, S., Retrospection and prospect of embeddedness theory–knowledge map analysis based on bibliometrics, J. Data, Inf. Manage., 2022, vol. 4, no. 1, pp. 57–70. https://doi.org/10.1007/s42488-022-00067-5

    Article  Google Scholar 

  3. Huang, H.Q., Yu, J., and Liao, X., Review on knowledge graphs, Comput. Sci. Appl., 2019, vol. 28, no. 6, pp. 1–12.

    Google Scholar 

  4. Liu, Z.Y., Research and implementation of knowledge graph mapping technology for public security intelligence analysis, MSc Thesis, Chengdu, China: Univ. of Electronic Science and Technology of China, 2019.

  5. Pendharkar, P.C. and Bhaskar, R., A hybrid Bayesian network-based multi-agent system and a distributed systems architecture for the drug crime knowledge management, Int. J. Inf. Technol. Decision Making, 2003, vol. 2, no. 4, pp. 557–576. https://doi.org/10.1142/s0219622003000872

    Article  Google Scholar 

  6. Keppens, J. and Schafer, B., Knowledge based crime scenario modelling, Expert Syst. Appl., 2006, vol. 30, no. 2, pp. 203–222. https://doi.org/10.1016/j.eswa.2005.07.011

    Article  Google Scholar 

  7. Phillips, P. and Lee, I., Mining top-k and bottom-k correlative crime patterns through graph representations, 2009 IEEE Int. Conf. on Intelligence and Security Informatics, Richardson, Tenn., 2009, IEEE, 2009, pp. 25–30. https://doi.org/10.1109/isi.2009.5137266

  8. Elezaj, O., Yayilgan, S.Y., Kalemi, E., Wendelberg, L., Abomhara, M., and Ahmed, J., Towards designing a knowledge graph-based framework for investigating and preventing crime on online social networks, E-Democracy–Safeguarding Democracy and Human Rights in the Digital Age. e-Democracy 2019, Katsikas, S. and Zorkadis, V., Eds., Communications in Computer and Information Science, vol. 1111, Cham: Springer, 2019, pp. 181–195. https://doi.org/10.1007/978-3-030-37545-4_12

  9. Zhang, W.X. and Zhu, Q.H., Research on semi-automatic domain ontology construction, Libr. Inf., 2011, no. 1, pp. 16–19.

  10. Zhao, M., Qin, H.P., Zhang, G.X., Lyu, Y.J., and Zhu, Y., TermTree and Knowledge Annotation Framework for Chinese Language Understanding, TR: 2020-KG-TermTree, Baidu, 2020.

    Google Scholar 

  11. Dozat, T. and Manning, C.D., Deep biaffine attention for neural dependency parsing, Proc. 5th Int. Conf. on Learning Representations, Toulon, France, 2017, Association for Computational Linguistics, 2017. https://openreview.net/forum?id=Hk95PK9le.

  12. Chang, P.-Ch., Tseng, H., Jurafsky, D., and Manning, C.D., Discriminative reordering with Chinese grammatical relations features, Proc. Third Workshop on Syntax and Structure in Statistical Translation-SSST ’09, Boulder, Colo., 2009, Stroudsburg, Pa.: Association for Computational Linguistics, 2009, pp. 51–59. https://doi.org/10.3115/1626344.1626351

  13. Su, J., SimBERT: Integrating retrieval and generation into BERT, 2020.

Download references

Funding

This study is partially supported by Jiangsu Social Science Foundation Project “Research on the Transformation of Public Security Intelligence Work Based on Big Data Technology” (19TQD004); Project on 2021 Outstanding young backbone teacher of Jiangsu Universities “Qinglan Project.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiu Mingyue.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu Mingyue, Zhang Xueying Establishment Method of Knowledge Graphs for Public Security Cases. Aut. Control Comp. Sci. 57, 543–551 (2023). https://doi.org/10.3103/S0146411623060056

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411623060056

Keywords:

Navigation