Knowledge Discovery Method from Text Big Data for Earthquake Emergency

被引：0

作者：

Liu T. ^{[1
,2
,3
]}

Zhang X. ^{[1
,2
,3
]}

Du P. ^{[1
,2
,3
]}

Du Q. ^{[4
]}

Li A. ^{[5
]}

Gong L. ^{[5
]}

机构：

[1] Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou

[2] National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou

[3] Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou

[4] School of Resource and Environmental Sciences, Wuhan University, Wuhan

[5] Zhejiang Academy of Surveying and Mapping, Hangzhou

来源：

Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2020年 / 45卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Big data; Community division; Complex networks; Earthquake emergency; Knowledge discovery;

D O I：

10.13203/j.whugis20200106

中图分类号：

学科分类号：

摘要：

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. © 2020, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.

引用

页码：1205 / 1213

页数：8

共 19 条

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