[目的/意义]重大突发事件是总体国家安全观的重要组成部分。为了更快速高效地应对重大突发事件,减少其对国家安全、社会稳定以及人民生命财产造成的威胁,提出构建重大突发事件领域的事理图谱,揭示重大突发事件演变的规律与逻辑,可以辅助相关部门对重大突发事件做出应对和决策。[方法/过程]选取国际关注且影响范围较广的2019冠状病毒疫情的媒体报道、研究报告和学术论文等,通过本体构建、事件抽取、事件关系抽取等步骤,构建重大突发事件事理图谱,并对事件演变的事理知识和传导路径进行分析。[结果/结论]研究结果表明,重大突发事件事理图谱可以揭示重大突发事件的演变逻辑与规律,进而从宏观层面把握事件的发展脉络,为重大突发事件的应对与治理提供科学准确的依据。
[Purpose/significance] Major emergencies are an important part of the overall national security concept. In order to respond to major emergencies more quickly and efficiently and reduce the threats to national security, social stability, and people's lives and properties, this article proposes to construct major event knowledge graph, it reveals the law and logic of the evolution of major emergencies, and can assist relevant departments in responding to and making decisions on major emergencies.[Method/process] This paper selected media reports, research reports and academic papers of the COVID-19 epidemic that had a wide range of international attention and influence, and used ontology construction and pattern matching methods for event extraction and event relationship extraction, to construct an affair map of major emergencies, and to analyze the affair knowledge and transmission paths of the evolution of the event.[Result/conclusion] The research results show that the affair map of major emergencies can reveal the evolution logic and laws of major emergencies, and then grasp the development context of events from a macro level, and provide scientific and accurate basis for the response and management of major emergencies.
[1] 孙娣.统筹发展和安全. 推进应急管理体系和能力现代化——2020年应急管理创新国际论坛综述[J]. 中国应急管理科学, 2020(12):86-95.
[2] 冯钧, 王云峰, 邬炜. 城市内涝事理图谱构建方法及应用[J]. 河海大学学报(自然科学版), 2020, 48(6):479-487.
[3] 孙鑫瑞, 孟雨, 王文乐. 基于知识图谱与目标检测的微博交通事件识别[J]. 数据分析与知识发现, 2020, 4(12):136-147.
[4] 胡欢. 面向热点话题的因果事理图谱构建及应用研究[D]. 青岛:青岛大学, 2020.
[5] 刘忠宝, 党建飞, 张志剑.《史记》历史事件自动抽取与事理图谱构建研究[J]. 图书情报工作, 2020, 64(11):116-124.
[6] MAO Q, LI X, PENG H, et al. Event prediction based on evolutionary event ontology knowledge[J]. Future generation computer systems, 2020, 115(2):76-89.
[7] NGUYEN H L, JUNG J J. Social event decomposition for constructing knowledge graph[J]. Future generation computer systems, 2019, 100(11):10-18.
[8] HEYVAERT P, CHAVES-FRAGA D, PRIYATNA F, et al. SAD Generator:eating our own dog food to generate KGs and websites for academic events[C]//European semantic Web conference. Cham:Springer, 2019, 11762(6):95-99.
[9] ZHAO Y, JIN X, WANG Y, et al. Semi-supervised auto-encoder based event detection in constructing knowledge graph for social good[C]//IEEE/WIC/ACM international conference on Web intelligence. Greece:Thessaloniki, 2019(10):478-485.
[10] 姜玉红, 鲍玉来.基于本体的领域概念语义描述研究——以中华武术术语为例[J]. 情报科学, 2020, 38(8):141-144, 169.
[11] USCHOLD M, KING M, MORALEE S, et al. The enterprise ontology[J]. The knowledge engineering review, 1998, 13(1):31-89.
[12] MAEDCHE A, STAAB S. Ontology learning[M]//Handbook on ontologies. Berlin:Springer, 2004:173-190.
[13] LAGOZE C, HUNTER J. The ABC ontology and model[C]//In:proceedings of the international conference on Dublin core and metadata applications. New York:ACM, 2001:160 -176.
[14] MEDITSKOS G, DASIOPOULOU S, EFSTATHIOU V, et al. Ontology patterns for complex activity modelling[C]//International work-shop on rules and rule markup languages for the semantic Web. Berlin:Springer, 2013:144-157.
[15] 刘宗田, 黄美丽, 周文, 等.面向事件的本体研究[J]. 计算机科学, 2009, 36(11):189-192.
[16] 于梦月, 申静, 翟军. 基于情景分析的我国开放政府数据元数据本体设计[J]. 情报科学, 2019, 37(2):143-148.
[17] 张海涛, 刘雅姝, 周红磊, 等. 情报智慧赋能:重大突发事件的智能协同决策[J]. 情报科学, 2020, 38(9):3-8.作者贡献说明:张海涛:提出研究思路与方法、数据分析、论文修订; 李佳玮:数据采集、分析处理, 论文初稿撰写; 刘伟利:数据收集、整理; 刘雅姝:论文修订。