Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from national and international initiatives which may...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nil Kilicay-Ergin, Adrian S. Barb
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-level initiatives policy context knowledge elicitation natural language processing semantic fusion deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/108e596e3d4b41b189613419b941c4d6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:108e596e3d4b41b189613419b941c4d6
record_format	dspace
spelling	oai:doaj.org-article:108e596e3d4b41b189613419b941c4d62021-11-11T15:07:06ZSemantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain10.3390/app1121100372076-3417https://doaj.org/article/108e596e3d4b41b189613419b941c4d62021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10037https://doaj.org/toc/2076-3417Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from national and international initiatives which may influence the incentives for local participants, but local initiatives reflect the local contextual elements of the city. Balanced assessment of smart city initiatives should include a systemic evaluation of the initiatives at multiple levels including the city, the country in which the city resides as well as at international level. In this paper, a knowledge elicitation methodology is presented for multi-granularity evaluation of policies and initiatives. The methodology is demonstrated on the evaluation of smart city initiatives generated at different administrative levels. Semantic networks are constructed using formal ontologies and deep learning methods for automatic semantic evaluation of initiatives to abstract knowledge found in text. Three smart city initiatives published by different administrative levels including international, national, and city level are evaluated in terms of relevance, coherence, and alignment of multi-level smart city initiatives. Experiments and analysis ultimately provide a holistic view of the problem which is necessary for decision makers and policy analysts of smart cities.Nil Kilicay-ErginAdrian S. BarbMDPI AGarticlemulti-level initiativespolicy contextknowledge elicitationnatural language processingsemantic fusiondeep learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10037, p 10037 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-level initiatives policy context knowledge elicitation natural language processing semantic fusion deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	multi-level initiatives policy context knowledge elicitation natural language processing semantic fusion deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Nil Kilicay-Ergin Adrian S. Barb Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain
description	Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from national and international initiatives which may influence the incentives for local participants, but local initiatives reflect the local contextual elements of the city. Balanced assessment of smart city initiatives should include a systemic evaluation of the initiatives at multiple levels including the city, the country in which the city resides as well as at international level. In this paper, a knowledge elicitation methodology is presented for multi-granularity evaluation of policies and initiatives. The methodology is demonstrated on the evaluation of smart city initiatives generated at different administrative levels. Semantic networks are constructed using formal ontologies and deep learning methods for automatic semantic evaluation of initiatives to abstract knowledge found in text. Three smart city initiatives published by different administrative levels including international, national, and city level are evaluated in terms of relevance, coherence, and alignment of multi-level smart city initiatives. Experiments and analysis ultimately provide a holistic view of the problem which is necessary for decision makers and policy analysts of smart cities.
format	article
author	Nil Kilicay-Ergin Adrian S. Barb
author_facet	Nil Kilicay-Ergin Adrian S. Barb
author_sort	Nil Kilicay-Ergin
title	Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain
title_short	Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain
title_full	Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain
title_fullStr	Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain
title_full_unstemmed	Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain
title_sort	semantic fusion with deep learning and formal ontologies for evaluation of policies and initiatives in the smart city domain
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/108e596e3d4b41b189613419b941c4d6
work_keys_str_mv	AT nilkilicayergin semanticfusionwithdeeplearningandformalontologiesforevaluationofpoliciesandinitiativesinthesmartcitydomain AT adriansbarb semanticfusionwithdeeplearningandformalontologiesforevaluationofpoliciesandinitiativesinthesmartcitydomain
_version_	1718437102050344960

Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

Ejemplares similares