|
|
Construction of a Knowledge Representation Model in Public Health Emergencies for Information Disclosure |
Xiang Yafan1, Liu Dongsu1, Ma Xubu1, Qin Chunxiu1, Shi Ying2 |
1.School of Economics and Management, Xidian University, Xi’an 710126 2.Xi’an Jiaotong University Library, Xi’an 710049 |
|
|
Abstract In recent years, there have been frequent public health emergencies that have posed great challenges to government emergency management. The timely disclosure of event information by the government helps eliminate public panic, which is crucial for epidemic prevention and control as well as socio-economic development. However, information on public health emergencies is scattered across a variety of locations in a fragmented, discontinuous, and incomplete manner. The description and organization of such a massive, diverse, and changing body of information that is not well-integrated is thus key to government emergency management. Therefore, this study is oriented toward information disclosure and integrates the knowledge graph and event knowledge graph, constructs a knowledge representation model of public health emergencies, and represents the core concepts and relationships within it. It also provides a method to organize information on public health emergencies that can both represent the spatial and temporal evolution of an epidemic and display epidemic information. The results show that this model has good vertical properties and displays a relatively rich conceptual relationship and attribute features, and most classes can be filled with examples. The knowledge representation model proposed in this study provides new ideas for the construction of an emergency knowledge base, thereby expanding the methodological system of emergency information organization. Further, it also helps in disclosing and releasing the value of stock information, satisfying the public’s information needs, and improving the effectiveness of emergency management.
|
Received: 05 September 2023
|
|
|
|
1 欧阳桃花, 郑舒文, 程杨. 构建重大突发公共卫生事件治理体系: 基于中国情景的案例研究[J]. 管理世界, 2020, 36(8): 19-32. 2 段尧清, 朱永迪, 蔡启宾, 等. 突发公共卫生事件下公众持续使用政府信息行为影响因素研究——基于QCA的触发路径及演化分析[J]. 信息资源管理学报, 2022, 12(3): 89-99. 3 Yin F L, Lv J H, Zhang X J, et al. COVID-19 information propagation dynamics in the Chinese Sina-microblog[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2676-2692. 4 中共中央关于制定国民经济和社会发展第十四个五年规划和二〇三五年远景目标的建议[EB/OL]. (2020-11-03) [2022-11-24]. http://www.gov.cn/zhengce/2020-11/03/content_5556991.htm. 5 刘晓娟, 王晨琳. 基于政务微博的信息公开与舆情演化研究——以新冠肺炎病例信息为例[J]. 情报理论与实践, 2021, 44(2): 57-63. 6 周维栋. 论突发公共卫生事件中信息公开的法律规制——兼论《传染病防治法》第38条的修改建议[J]. 行政法学研究, 2021(4): 147-161. 7 储节旺, 郭春侠. 突发重大传染病疫情数据管理实践及其思考——以新型冠状病毒肺炎疫情为例[J]. 情报理论与实践, 2020, 43(5): 1-8. 8 赵一鸣. 知识图谱是一种知识组织系统吗?[J]. 图书情报知识, 2017(5): 2. 9 张永娟, 刘炜, 于建荣, 等. 基于IIIF和语义知识图谱的印章资源整合与知识发现研究[J]. 图书情报工作, 2020, 64(7): 127-135. 10 Nickel M, Murphy K, Tresp V, et al. A review of relational machine learning for knowledge graphs[J]. Proceedings of the IEEE, 2016, 104(1): 11-33. 11 Long J W, Chen Z P, He W B, et al. An integrated framework of deep learning and knowledge graph for prediction of stock price trend: an application in Chinese stock exchange market[J]. Applied Soft Computing, 2020, 91: 106205. 12 Shen Y, Yuan K Q, Dai J C, et al. KGDDS: a system for drug-drug similarity measure in therapeutic substitution based on knowledge graph curation[J]. Journal of Medical Systems, 2019, 43(4): Article No.92. 13 孙飞鹏, 于淼, 汤京淑. 基于知识图谱的汉语词汇学习资源推荐研究——以HSK三级词汇为例[J]. 现代教育技术, 2021, 31(1): 76-82. 14 黄恒琪, 于娟, 廖晓, 等. 知识图谱研究综述[J]. 计算机系统应用, 2019, 28(6): 1-12. 15 牛力, 高晨翔, 张宇锋, 等. 发现、重构与故事化: 数字人文视角下档案研究的路径与方法[J]. 中国图书馆学报, 2021, 47(1): 88-107. 16 张吉祥, 张祥森, 武长旭, 等. 知识图谱构建技术综述[J]. 计算机工程, 2022, 48(3): 23-37. 17 王雪鹏, 刘康, 何世柱, 等. 基于网络语义标签的多源知识库实体对齐算法[J]. 计算机学报, 2017, 40(3): 701-711. 18 王杰, 李晓楠, 李冠宇. 基于自适应注意力机制的知识图谱补全算法[J]. 计算机科学, 2022, 49(7): 204-211. 19 周京艳, 刘如, 李佳娱, 等. 情报事理图谱的概念界定与价值分析[J]. 情报杂志, 2018, 37(5): 31-36, 42. 20 刘雅姝, 栾宇, 周红磊, 等. 基于事理图谱的重大突发事件动态演变研究[J]. 图书情报工作, 2022, 66(10): 143-151. 21 李培峰, 黄一龙, 朱巧明. 使用全局优化方法识别中文事件因果关系[J]. 清华大学学报(自然科学版), 2017, 57(10): 1042-1047. 22 吴忠, 夏志杰. 面向轨道交通应急管理的信息集成及应用研究[J]. 交通运输系统工程与信息, 2011, 11(5): 48-54. 23 王君. 城市防灾应急信息数据同步整合系统优化设计[J]. 灾害学, 2019, 34(2): 173-177. 24 Wieland M, Pittore M. A spatio-temporal building exposure database and information life-cycle management solution[J]. ISPRS International Journal of Geo-Information, 2017, 6(4): 114. 25 裘江南, 师花艳, 叶鑫, 等. 基于事件的定性知识表示模型[J]. 系统工程, 2009, 27(10): 1-8. 26 杨峰, 张抗抗, 徐如志, 等. 基于本体的应急预案知识表示模型研究[C]// 第十一届中国管理科学学术年会论文集. 北京: 中国优选法统筹法与经济数学研究会, 2009: 670-673. 27 Amailef K, Lu J. Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services[J]. Decision Support Systems, 2013, 55(1): 79-97. 28 González-Eras A, Dos Santos R, Aguilar J, et al. Ontological engineering for the definition of a COVID-19 pandemic ontology[J]. Informatics in Medicine Unlocked, 2022, 28: 100816. 29 李纲, 王施运, 毛进, 等. 面向态势感知的国家安全事件图谱构建研究[J]. 情报学报, 2021, 40(11): 1164-1175. 30 刘静, 凌以民. 我国政务新媒体矩阵的建设分析[J]. 出版广角, 2020(19): 23-25. 31 唐要家, 林梓鹏. 基于产权理论的智慧城市信息共享机制研究[J]. 情报杂志, 2019, 38(2): 166-171. 32 汤志伟, 赵迪, 罗伊晗. 公共危机事件中政务短视频公众使用的实证研究——基于新冠肺炎疫情[J]. 电子政务, 2020(8): 2-14. 33 van Hage W R, Malaisé V, Segers R, et al. Design and use of the Simple Event Model (SEM)[J]. Journal of Web Semantics, 2011, 9(2): 128-136. 34 Ulrich H D. The SUMO system: an overview[M]// Methods in Molecular Biology. Totowa: Humana Press, 2009: 3-16. 35 王芳, 杨京, 徐路路. 面向火灾应急管理的本体构建研究[J]. 情报学报, 2020, 39(9): 914-925. 36 安璐, 李倩. 基于热点主题识别的突发事件次生衍生事件探测[J]. 情报资料工作, 2020, 41(6): 26-35. 37 胡象明, 陈一帆. 突发公共卫生事件社会稳定风险的生成逻辑[J]. 行政论坛, 2020, 27(3): 72-79. 38 张海蛟. 航班延误引发的机场次生衍生事件及其链式效应分析[D]. 南京: 南京航空航天大学, 2016. 39 沙勇忠, 刘红芹. 公共危机的利益相关者分析模型[J]. 科学 经济 社会, 2009, 27(1): 58-61. 40 邓三鸿, 刘喜文, 蒋勋. 基于利益相关者理论的突发事件案例知识库构建研究[J]. 图书与情报, 2015(3): 1-8. 41 韩玮, 陈樱花, 陈安. 基于KANO模型的突发公共卫生事件信息公开的公众需求研究[J]. 情报理论与实践, 2020, 43(5): 9-16. 42 李月琳, 王姗姗. 面向突发公共卫生事件的相关信息发布特征分析[J]. 图书与情报, 2020(1): 27-33, 50. 43 何琳, 陈雅玲, 孙珂迪. 面向先秦典籍的知识本体构建技术研究[J]. 图书情报工作, 2020, 64(7): 13-19. 44 刘雅姝. 多维视角的重大突发事件演变机理及应对策略研究[D]. 长春: 吉林大学, 2021. 45 Tartir S, Arpinar I B, Moore M, et al. OntoQA: metric-based ontology quality analysis[C]// Proceedings of IEEE ICDM 2005 Workshop on Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources. Piscataway: IEEE, 2005: 1-9. |
|
|
|