Automatic Semantic Description Extraction from Social Big Data for Emergency Management

来源 :系统科学与系统工程学报(英文版) | 被引量 : 0次 | 上传用户:sirius1394
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
Emergency events are unexpected and dangerous situations which the authorities must manage and respond to as quickly as possible.The main objectives of emergency management are to provide human safety and security,and Social Big Data (SBD) offers an important information source,created directly from eyewitness reports,to assist with these issues.However,the manual extraction of hidden meaning from SBD is both time-consuming and labor-intensive,which are major drawbacks for a process that needs accurate information to be produced in real-time.The solution is an automatic approach to knowledge discovery,and we propose a semantic description technique based on the use of triple store indexing for named entity recognition and relation extraction.Our technique can discover hidden SBD information more effectively than traditional approaches,and can be used for intelligent emergency management.
其他文献
期刊
目的:慢性脑缺血是由于长期脑血流灌注不足所致,它是临床上常见的脑损伤之一,是阿尔茨海默病和血管性痴呆(VD)等多种以认知功能障碍为基本特征疾病发展过程中的一个共同病理过
基于地质、测录井和实验分析资料,运用建立的页岩气储层定量表征方法,开展了彭水地区上奥陶统五峰组—下志留统龙一段常压页岩气储层研究,认为①—⑤小层内的优质页岩储集空
以中国石化川南地区五峰组—龙马溪组典型取心井X井为例,基于岩心观察、地化分析、X衍射、物性测试、氩离子抛光—扫描电镜、生烃模拟等多种测试手段,运用“源储耦合”研究思
Recently, organ-on-chips have become a fast-growing research field with the widespread development of microfluidic chips and synthetic materials in tissue engin