Automatic Semantic Description Extraction from Social Big Data for Emergency Management

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.