DisKnow: a social-driven disaster support knowledge extraction system
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/21056 |
Resumo: | 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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DisKnow: a social-driven disaster support knowledge extraction systemDisaster managementNatural language processingInformation extractionCrowdsourcingAutomatic knowledge base constructionKnowledge graphsThis 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.MDPI AG2021-01-04T11:17:10Z2020-01-01T00:00:00Z20202021-01-04T11:12:37Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/21056eng2076-341710.3390/app10176083Boné, J.Dias, M.Ferreira, J. C.Ribeiro, R.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:47:44Zoai:repositorio.iscte-iul.pt:10071/21056Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:23:11.503535Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
DisKnow: a social-driven disaster support knowledge extraction system |
title |
DisKnow: a social-driven disaster support knowledge extraction system |
spellingShingle |
DisKnow: a social-driven disaster support knowledge extraction system Boné, J. Disaster management Natural language processing Information extraction Crowdsourcing Automatic knowledge base construction Knowledge graphs |
title_short |
DisKnow: a social-driven disaster support knowledge extraction system |
title_full |
DisKnow: a social-driven disaster support knowledge extraction system |
title_fullStr |
DisKnow: a social-driven disaster support knowledge extraction system |
title_full_unstemmed |
DisKnow: a social-driven disaster support knowledge extraction system |
title_sort |
DisKnow: a social-driven disaster support knowledge extraction system |
author |
Boné, J. |
author_facet |
Boné, J. Dias, M. Ferreira, J. C. Ribeiro, R. |
author_role |
author |
author2 |
Dias, M. Ferreira, J. C. Ribeiro, R. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Boné, J. Dias, M. Ferreira, J. C. Ribeiro, R. |
dc.subject.por.fl_str_mv |
Disaster management Natural language processing Information extraction Crowdsourcing Automatic knowledge base construction Knowledge graphs |
topic |
Disaster management Natural language processing Information extraction Crowdsourcing Automatic knowledge base construction Knowledge graphs |
description |
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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01T00:00:00Z 2020 2021-01-04T11:17:10Z 2021-01-04T11:12:37Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/21056 |
url |
http://hdl.handle.net/10071/21056 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 10.3390/app10176083 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI AG |
publisher.none.fl_str_mv |
MDPI AG |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134793656434688 |