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Disaster management ontology- an ontological approach to disaster management automation
The geographical location of any region, as well as large-scale environmental changes caused by a variety of factors, invite a wide range of disasters. Floods, droughts, earthquakes, cyclones, landslides, tornadoes, and cloudbursts are all common natural disasters that destroy property and kill peop...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199055/ https://www.ncbi.nlm.nih.gov/pubmed/37208434 http://dx.doi.org/10.1038/s41598-023-34874-6 |
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author | Shukla, Deepika Azad, Hiteshwar Kumar Abhishek, Kumar Shitharth, S. |
author_facet | Shukla, Deepika Azad, Hiteshwar Kumar Abhishek, Kumar Shitharth, S. |
author_sort | Shukla, Deepika |
collection | PubMed |
description | The geographical location of any region, as well as large-scale environmental changes caused by a variety of factors, invite a wide range of disasters. Floods, droughts, earthquakes, cyclones, landslides, tornadoes, and cloudbursts are all common natural disasters that destroy property and kill people. On average, 0.1% of the total deaths globally in the past decade have been due to natural disasters. The National Disaster Management Authority (NDMA), a branch of the Ministry of Home Affairs, plays an important role in disaster management in India by taking responsibility for risk mitigation, response, and recovery from all natural and man-made disasters. This article presents an ontology-based disaster management framework based on the NDMA’s responsibility matrix. This ontological base framework is named as Disaster Management Ontology (DMO). It aids in task distribution among necessary authorities at various stages of a disaster, as well as a knowledge-driven decision support system for financial assistance to victims. In the proposed DMO, ontology has been used to integrate knowledge as well as a working platform for reasoners, and the Decision Support System (DSS) ruleset is written in Semantic Web Rule Language (SWRL), which is based on the First Order Logic (FOL) concept. In addition, OntoGraph, a class view of taxonomy, is used to make taxonomy more interactive for users. |
format | Online Article Text |
id | pubmed-10199055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101990552023-05-21 Disaster management ontology- an ontological approach to disaster management automation Shukla, Deepika Azad, Hiteshwar Kumar Abhishek, Kumar Shitharth, S. Sci Rep Article The geographical location of any region, as well as large-scale environmental changes caused by a variety of factors, invite a wide range of disasters. Floods, droughts, earthquakes, cyclones, landslides, tornadoes, and cloudbursts are all common natural disasters that destroy property and kill people. On average, 0.1% of the total deaths globally in the past decade have been due to natural disasters. The National Disaster Management Authority (NDMA), a branch of the Ministry of Home Affairs, plays an important role in disaster management in India by taking responsibility for risk mitigation, response, and recovery from all natural and man-made disasters. This article presents an ontology-based disaster management framework based on the NDMA’s responsibility matrix. This ontological base framework is named as Disaster Management Ontology (DMO). It aids in task distribution among necessary authorities at various stages of a disaster, as well as a knowledge-driven decision support system for financial assistance to victims. In the proposed DMO, ontology has been used to integrate knowledge as well as a working platform for reasoners, and the Decision Support System (DSS) ruleset is written in Semantic Web Rule Language (SWRL), which is based on the First Order Logic (FOL) concept. In addition, OntoGraph, a class view of taxonomy, is used to make taxonomy more interactive for users. Nature Publishing Group UK 2023-05-19 /pmc/articles/PMC10199055/ /pubmed/37208434 http://dx.doi.org/10.1038/s41598-023-34874-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Shukla, Deepika Azad, Hiteshwar Kumar Abhishek, Kumar Shitharth, S. Disaster management ontology- an ontological approach to disaster management automation |
title | Disaster management ontology- an ontological approach to disaster management automation |
title_full | Disaster management ontology- an ontological approach to disaster management automation |
title_fullStr | Disaster management ontology- an ontological approach to disaster management automation |
title_full_unstemmed | Disaster management ontology- an ontological approach to disaster management automation |
title_short | Disaster management ontology- an ontological approach to disaster management automation |
title_sort | disaster management ontology- an ontological approach to disaster management automation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199055/ https://www.ncbi.nlm.nih.gov/pubmed/37208434 http://dx.doi.org/10.1038/s41598-023-34874-6 |
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