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Title: DisKnow: a social-driven disaster support knowledge extraction system
Authors: Boné, J.
Dias, M.
Ferreira, J. C.
Ribeiro, R.
Keywords: Disaster management
Natural language processing
Information extraction
Automatic knowledge base construction
Knowledge graphs
Issue Date: 2020
Publisher: MDPI AG
Abstract: 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.
Peer reviewed: yes
DOI: 10.3390/app10176083
ISSN: 2076-3417
Accession number: WOS:000569973900001
Appears in Collections:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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