专题:多模态数据驱动的安全态势感知研究

多模态数据驱动的公共安全事件事理图谱研究

  • 郭宇 ,
  • 刘芳妤 ,
  • 张传洋 ,
  • 杨梦晴
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  • 1 吉林大学商学与管理学院, 长春 130015;
    2 吉林大学信息资源研究中心, 长春 130015;
    3 吉林大学国家发展与安全研究院, 长春 130015;
    4 南京师范大学新闻与传播学院, 南京 210023
郭宇,副教授,博士,博士生导师;刘芳妤,硕士研究生;张传洋,博士研究生;杨梦晴,讲师,博士,通信作者,E-mail:mqyang@nnu.edu.cn。

收稿日期: 2024-06-21

  修回日期: 2024-08-01

  网络出版日期: 2024-12-23

基金资助

本文系国家社会科学基金一般项目“多模态网络数据安全态势感知与风险协同治理机制研究”(项目编号:23BTQ076)研究成果之一。

Research on the Event Knowledge Graph of Public Security Event Driven by Multimodal Data

  • Guo Yu ,
  • Liu Fangyu ,
  • Zhang Chuanyang ,
  • Yang Mengqing
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  • 1 School of business and management, Jilin University, Changchun 130015;
    2 Information Resources Research Center, Jilin University, Changchun 130015;
    3 Institute of National Development and Security Studies, Jilin University, Changchun 130015;
    4 School of Journalism and Communication, Nanjing Normal University, Nanjing 210023

Received date: 2024-06-21

  Revised date: 2024-08-01

  Online published: 2024-12-23

Supported by

This work is supported by the general project of National Social Science Fund of China titled “Research on Multimodal Network Data Security Situation Awareness and Risk Collaborative Governance Mechanism” (Grant No. 23BTQ076).

摘要

[目的/意义] 探索多模态数据驱动的公共安全事件事理图谱构建方法,挖掘多模态公共安全事件的内在逻辑与演变规律,实现对公共安全事件的深入分析和理解,增强公共安全事件的预防和控制能力。[方法/过程] 获取官方通报、新闻报道、期刊论文的多模态公共安全事件数据,通过构建公共安全本体、事件抽取、事件关系抽取等步骤,构建多模态公共安全事件事理图谱,并基于事理图谱进行事件的时序演化和因果逻辑分析。[结果/结论] 研究表明多模态数据的公共安全事件事理图谱可以揭示公共安全事件的事理逻辑,并为公共安全事件的预防和应对提供科学依据。

本文引用格式

郭宇 , 刘芳妤 , 张传洋 , 杨梦晴 . 多模态数据驱动的公共安全事件事理图谱研究[J]. 图书情报工作, 2024 , 68(24) : 15 -26 . DOI: 10.13266/j.issn.0252-3116.2024.24.002

Abstract

[Purpose/Significance] This paper aims to explore the construction method of public security event graph based on multimodal data, excavate the evolutionary law and internal logic of multimodal public security events, achieve a deep analysis and understanding of public safety events and enhance the ability of event prevention and control. [Method/Process] This paper obtained multimodal public safety event data including official bulletins, news reports and journal articles. By constructing public security ontology, event extraction and event relationship extraction, the multimodal public security event graph was constructed, and the temporal evolution and causal logic analysis of public security events were carried out based on the event graph. [Result/Conclusion] The research shows that the public security event graph based on multimodal data can reveal the event logic of public security events and provide scientific basis for the prevention and response of public security events.

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