This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.

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Disaster management,Natural language processing,Information extraction,Crowdsourcing,Automatic knowledge base construction,Knowledge graphs

• Computer and Information Sciences - Natural Sciences
• Physical Sciences - Natural Sciences
• Chemical Sciences - Natural Sciences
• Other Natural Sciences - Natural Sciences
• Civil Engineering - Engineering and Technology
• Chemical Engineering - Engineering and Technology
• Materials Engineering - Engineering and Technology