刘涛, 张晓辉, 杜萍, 杜清运, 李爱勤, 龚丽芳. 文本大数据中地震应急的知识发现方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(8): 1205-1213. DOI: 10.13203/j.whugis20200106
引用本文: 刘涛, 张晓辉, 杜萍, 杜清运, 李爱勤, 龚丽芳. 文本大数据中地震应急的知识发现方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(8): 1205-1213. DOI: 10.13203/j.whugis20200106
LIU Tao, ZHANG Xiaohui, DU Ping, DU Qingyun, LI Aiqin, GONG Lifang. Knowledge Discovery Method from Text Big Data for Earthquake Emergency[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1205-1213. DOI: 10.13203/j.whugis20200106
Citation: LIU Tao, ZHANG Xiaohui, DU Ping, DU Qingyun, LI Aiqin, GONG Lifang. Knowledge Discovery Method from Text Big Data for Earthquake Emergency[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1205-1213. DOI: 10.13203/j.whugis20200106

文本大数据中地震应急的知识发现方法

Knowledge Discovery Method from Text Big Data for Earthquake Emergency

  • 摘要: 构建地震应急的知识发现模型是地震应急知识领域的核心科学问题之一,如何在种类繁多、内容繁杂的数据中,研究减少先验知识依赖和支持的地震应急知识发现至关重要。提出了一种文本大数据中地震应急的知识发现模型。首先,收集与地震应急相关的学术文献数据集和社交媒体数据集;然后,利用CiteSpace分析工具及形式概念分析方法提取高频关键词及其关联关系,以词频联系作为它们之间关系的强度,并构建地震应急知识的复杂网络,以对网络进行社区划分研究,实现地震应急的知识发现。实验结果表明,该模型能够发现地震应急的相关知识,特别是能够发现领域专家关注较少的知识点,为地震应急提供知识决策支持。

     

    Abstract:
      Objectives  Earthquake occurred frequently in China. More knowledge needs to be discovered to take timely emergency actions to mitigate earthquake damage. Thus the construction method of knowledge discovery model for earthquake emergency is one of the core research issues in earthquake emergency field. It is worth studying how to realize knowledge discovery for earthquake emergency from abundant and complicated data under less support of prior knowledge.
      Methods   We put forward a kind of text big data based knowledge discovery model for earthquake emergency. First, text data associated with earthquake emergency is collected, including both academic document data (CNKI(China national knowledge infrastructure) data) sets and social media data sets(Weibo data). Then CiteSpace tool and formal concept analysis method are utilized to extract the high-frequency keywords and their linkages, the linkages’ frequency between keywords as linkage strength, a complex network of earthquake emergency knowledge is established for community classification. After community dividing, several big communities can be divided from the former established complex network.
      Results   From big communities’ further analysis : (1) in seismic resistance design domain, the discovered knowledge has high coherence with expert knowledge; (2) in earthquake rescue domain, the discovered knowledge shows that China Earthquake Administration have responsibility to afford emergency information services as well as public opinion guiding; (3)in geological hazards domain, the discovered knowledge founds the vegetation restoration in geological disaster area should be paid more attention; (4) the knowledge discovered from academic document data and social media data can be complementary.
      Conclusions   The experiment result shows that the model can find relevant knowledge, especially those experts rarely concern.

     

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