面向公共安全事件的网络文本大数据结构化研究

doi:10.12082/dqxxkx.2019.180680

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (1): 2-13.doi: 10.12082/dqxxkx.2019.180680

所属专题：地理大数据

• 地理大数据时空模式挖掘的方法与应用研究 • 上一篇下一篇

面向公共安全事件的网络文本大数据结构化研究

裴韬^1,^2,⁷(), 郭思慧^1,², 袁烨城^1,^*(), 张雪英^3,⁷, 袁文¹, 高昂⁴, 赵志远⁵, 薛存金⁶

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 南京师范大学虚拟地理环境教育部重点实验室,南京 210023
4. 中国标准化研究院,北京 100088
5. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
6. 中国科学院遥感与数字地球研究所,北京 100094
7. 江苏省地理信息资源开发与利用协同创新中心,南京 210023

收稿日期:2018-12-01 修回日期:2018-12-24 出版日期:2019-01-20 发布日期:2019-01-20
通讯作者: 袁烨城 E-mail:peit@lreis.ac.cn;yuanyc@lreis.ac.cn
作者简介:
作者简介：裴韬（1972-）,男,江苏扬州人,研究员,研究方向为地理大数据挖掘。E-mail:peit@lreis.ac.cn
基金资助:
国家自然科学基金项目（41525004、 41421001）

Public Security Event Themed Web Text Structuring

Tao PEI^1,^2,⁷(), Sihui GUO^1,², Yecheng YUAN^1,^*(), Xueying ZHANG^3,⁷, Wen YUAN¹, Ang GAO⁴, Zhiyuan ZHAO⁵, Cunjin XUE⁶

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
4. China National Institute of Standardization, Beijing 100088, China
5. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of Wuhan University, Wuhan 430079, China
6. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
7. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2018-12-01 Revised:2018-12-24 Online:2019-01-20 Published:2019-01-20
Contact: Yecheng YUAN E-mail:peit@lreis.ac.cn;yuanyc@lreis.ac.cn
Supported by:
National Natural Science Foundation of China, No.41525004,41421001

摘要/Abstract

摘要：

网络文本中所包含的相关信息目前已成为公共安全事件紧急救援与影响评估的重要信息源。现有的方法虽然可定向地提取文本信息中事件的各类要素信息,但由于缺乏面向事件的整体建模与解析框架,难以从网络文本中获取系统的事件要素的结构化信息,即所提取的事件要素信息要么不够完整,要么与目标事件不匹配,由此产生的遗漏与谬误难以支撑针对公共安全事件信息的系统分析。为解决该问题,本文提出了面向公共安全事件的网络文本大数据结构化理论框架,首先,建立了公共安全事件的语义框架,并以地震事件为例构建了相应的结构化表结构;其次,应用训练语料的关联标注解决了事件要素与事件无法匹配的难点;最后,通过使用可融合关联信息的文本解析算法,系统提取了事件类型、事件名称、事件时间、事件位置及其他属性,基本实现了网络文本中不同事件信息的结构化。本文以云南邵通鲁甸地震为例,展示了地震事件的网络文本信息的结构化过程与结果,为分析地震所受的关注程度以及救援状况提供了重要参考。在上述研究的基础上,开发了面向公共安全事件的网络文本信息挖掘系统,展示了地震事件文本的结构化解析以及由此实施的事件关注度分析。

关键词: 语义框架, 文本解析, 事件关注度, 地震事件, 空间搜索引擎

Abstract:

The information of public security event contained in text can be the data source of the evaluation and the relief if it can be structured into a relational database. Although previous research can extract the information of events into different attributes, the determination on the attribution of the attribute information to specific event remains unsolved. To solve the problem, this paper proposes a theoretical frame of public security event themed web text structuring, which is composed of three parts. First, an event semantic model is used to construct the seismic event semantic framework which defines abstract elements of event and their semantic relationships. Taking seismicity as an example, spatial element, time element, attribute element, source element are defined as basic elements. Spatial element includes earthquake latitude, longitude, depth and location. Attribute element is further subdivided into four sub-elements: Cause, result, behavior and influence element. Next, an annotation system is applied to typical event materials to label semantic elements, e.g. the place name where an earthquake took place, that is, instantiation of the abstract elements. The key to this step is labeling the relations between elements and specific event. Finally, the event text is structured into event type, event name, event time, event location and other attributes by using the text information extraction algorithm. The algorithm used the labeled materials in the last step as training data to optimize parameters, which can incorporate linked information. The extracted event text (e.g. words, phrases) finally is normalized to structured information for further analysis. An event information mining platform following the whole frame is developed, which includes the modules of webpage searching, text cleaning, event information extraction, visualization and analyzing. The platform processed the whole Chinese webpages of 2014 and found 85 506 seismicity reports. Taking Yunnanludian earthquake as an example, we display the structuring process and result of related web text, which can be the important reference for the relief of the disaster and the analysis of public concern. With the platform, we can demonstrate the seismic text structuring result and its social concern across China, which can be a new tool of event information mining and analyzing.

Key words: semantic framework, text parsing, social concern about events, seismic events, spatial search engine

裴韬, 郭思慧, 袁烨城, 张雪英, 袁文, 高昂, 赵志远, 薛存金. 面向公共安全事件的网络文本大数据结构化研究[J]. 地球信息科学学报, 2019, 21(1): 2-13.DOI:10.12082/dqxxkx.2019.180680

Tao PEI, Sihui GUO, Yecheng YUAN, Xueying ZHANG, Wen YUAN, Ang GAO, Zhiyuan ZHAO, Cunjin XUE. Public Security Event Themed Web Text Structuring[J]. Journal of Geo-information Science, 2019, 21(1): 2-13.DOI:10.12082/dqxxkx.2019.180680

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	主题分类及提取	时间与地名提取	属性信息抽取	空间关系提取
基于规则的方法	Rafea等^[5]、Petkos等^[6]	谭红叶等^[7]、肖计划^[8]	丁效^[9]、吴家皋等^[10]	马林兵等^[11]、乐小虬等^[12]、乐小虬等^[13]、蒋文明^[14]
最大熵模型	Li等^[15]、李荣陆等^[15]、肖雪^[17]	王江伟^[18]、王胜等^[19]、钱晶等^[20]	-	Kambhatla等^[21]
支持向量机	Kumar等^[22]、冯永等^[23]、王金华等^[24]	李丽双等^[25]、李丽双等^[26]、唐晋韬^[27]	周凡坤^[28]	Wang等^[29]、Jiang等^[30]、Bunescu等^[31]、ZHOU等^[32]、项乐安^[33]
马尔科夫模型	张春元^[34]、梁吉光等^[35]、史庆伟等^[36]	马龙^[37]、高国洋等^[38]、邬伦等^[39]	Scheffer等^[40]、Ojokoh等^[41]、梁吉光等^[35]、Zhou等^[42]、吴家皋等^[10]	董静等^[43]、张春菊^[44]
贝叶斯分类	Sankaranarayanan等^[45],路金泉等^[46]、武建军等^[47]、	刘杰^[48]	-	顾雪峰^[49]
神经网络	杨俊等^[50]、吕淑宝等^[51]、郭东亮等^[52]	欧嘉致等^[53]	李帅等^[54]、吕国英等^[55]、叶开^[56]	-
最邻近方法KNN	Jiang等^[57]、周庆平等^[58]、戚后林等^[59]	-	-	-

代码	名称	代码	名称	代码	名称	代码	名称
0100	自然灾害	0200	事故灾难	0300	公共卫生事件	0400	社会安全事件
0101	水旱灾害	0201	战争和暴力	0301	传染病疫情	0401	恐怖袭击事件
0102	气象灾害	0202	工矿商贸安全事故	0302	群体性不明原因疾病	0402	重大刑事案件
0103	地震灾害	0203	交通运输安全事故	0303	食品安全和职业危害	0403	经济安全事件
0104	地质灾害	0204	城市生命线事故	0304	动物疫情	0404	涉外突发事件
0105	海洋灾害	0205	通讯安全事故	0399	其他	0405	规模较大的群体性事件
0106	生物灾害	0206	环境污染和生态破坏			0406	民族宗教
0107	森林草原火灾	0207	严重火灾			0407	反政府和反社会主义骚乱暴动
0108	宇宙灾害	0208	中毒事件			0499	其他
0199	其他	0209	急性化学事故
		0210	放射事故
		0211	医药事故
		0212	探险遇难
		0213	旅游事故
		0299	其他

要素类型名称		地震事件要素	数据类型
类型		事件类型	字符型
对象		地震名称	字符型
编号		记录编号	整数型
时间		发震时间	年-月-日-时-分-秒（时间型）
空间		发震地点	字符型
		震中纬度	实数型
		震中经度	实数型
		地震深度	实数型
属性	原因	发震原因	字符型
	结果	地震震级	实数型
		地震烈度	整型
		死亡人数	整型
		受伤人数	整型
		失踪人数	整型
		受灾人数	整型
		安置人数	整型
		建筑损坏	整型
		经济损失	实数型
		受灾范围	实数型
		记录时间	年-月-日-时-分-秒（时间型）
	行为	震感程度	字符型
	行为	天气状况	字符型
		救援方式	捐款/实地/安置/重建/
	影响	救援人数	整型
		救援资金	实数型
来源	发布时间	发布时间	年-月-日-时-分-秒（时间型）
	网址	信息网址	字符型
	可靠性	信息可靠性级别	国家媒体/地方媒体/企业媒体

属性列	记录1	记录2	记录3	……
事件类型	地震	地震	地震	……
地震名称	邵通鲁甸地震	邵通鲁甸地震	邵通鲁甸地震
记录编号	36 786	64 873	4783
发震时间	2014-08-03-00-00-00	2014-08-03-16-30-00	2014-08-03-00-00-00
发震地点	云南省昭通市鲁甸县	云南省昭通市鲁甸县	云南省昭通市鲁甸县
震中经度	-	103.3	103.725
震中纬度	-	27.1	27.34
震源深度	-	12 000	12 000
发震原因	-	-	-
地震震级	6.5	6.5	6.5
地震烈度	-	-	-
死亡人数	367	589	617
受伤人数	1801	2401	3143
失踪人数	5	9	112
受灾人数	-	1 088 400	1 088 400
安置人数	-	229 700	229 700
建筑损坏	-	80 900	-
经济损失	-	-	-
受灾范围	-	-	-
记录时间	2014-08-03	2014-08-06-10-30-00	2014-08-21-00-00-00
震感程度	-	-	-
天气状况	-	-	-
救援方式	捐款	安置	安置
救援人数	-	-	-
救援资金	-	-	-
发布时间	2014-08-05-09-57-00	2014-08-06-12-58-08	2014-08-21-22-47-42
信息网址	http://dl.sina.com.cn/news/wenti/2014-08-05/095732477.html	http://news.iqilu.com/china/gedi/2014/0806/2093139.shtml	http://news.china.com.cn/rollnews/education/live/2014-08/21/content_28320532.htm
可靠性	企业媒体	地方媒体	国家媒体

面向公共安全事件的网络文本大数据结构化研究

Public Security Event Themed Web Text Structuring

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