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Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning

Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to bui...

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Detalles Bibliográficos
Autores principales: Yang, Qing, Zuo, Chen, Liu, Xingxing, Yang, Zhichao, Zhou, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246749/
https://www.ncbi.nlm.nih.gov/pubmed/32397529
http://dx.doi.org/10.3390/ijerph17093312
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author Yang, Qing
Zuo, Chen
Liu, Xingxing
Yang, Zhichao
Zhou, Hui
author_facet Yang, Qing
Zuo, Chen
Liu, Xingxing
Yang, Zhichao
Zhou, Hui
author_sort Yang, Qing
collection PubMed
description Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response.
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spelling pubmed-72467492020-06-10 Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning Yang, Qing Zuo, Chen Liu, Xingxing Yang, Zhichao Zhou, Hui Int J Environ Res Public Health Article Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response. MDPI 2020-05-09 2020-05 /pmc/articles/PMC7246749/ /pubmed/32397529 http://dx.doi.org/10.3390/ijerph17093312 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Qing
Zuo, Chen
Liu, Xingxing
Yang, Zhichao
Zhou, Hui
Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning
title Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning
title_full Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning
title_fullStr Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning
title_full_unstemmed Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning
title_short Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning
title_sort risk response for municipal solid waste crisis using ontology-based reasoning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246749/
https://www.ncbi.nlm.nih.gov/pubmed/32397529
http://dx.doi.org/10.3390/ijerph17093312
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