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Boné, J., Dias, M., Ferreira, J. C. & Ribeiro, R. (2020). DisKnow: a social-driven disaster support knowledge extraction system. Applied Sciences. 10 (17)

J. Boné et al.,  "DisKnow: a social-driven disaster support knowledge extraction system", in Applied Sciences, vol. 10, no. 17, 2020

@article{boné2020_1713892182665,
	author = "Boné, J. and Dias, M. and Ferreira, J. C. and Ribeiro, R.",
	title = "DisKnow: a social-driven disaster support knowledge extraction system",
	journal = "Applied Sciences",
	year = "2020",
	volume = "10",
	number = "17",
	doi = "10.3390/app10176083",
	url = "https://www.mdpi.com/"
}

TY  - JOUR
TI  - DisKnow: a social-driven disaster support knowledge extraction system
T2  - Applied Sciences
VL  - 10
IS  - 17
AU  - Boné, J.
AU  - Dias, M.
AU  - Ferreira, J. C.
AU  - Ribeiro, R.
PY  - 2020
SN  - 2076-3417
DO  - 10.3390/app10176083
UR  - https://www.mdpi.com/
AB  - 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.
ER  -