What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities

Autores
Lezoche, Mario; Torres, Diego
Año de publicación
2025
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The Cognitive Digital Twin (CDT) is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. Integrating machine learning algorithms and artificial intelligence allows CDTs to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of CDTs are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, semantic web technologies can facilitate advanced analytics and machine learning within CDTs. This article offers a rapid analysis of how Semantic Web approaches can support several aspects of CDT models.
Materia
Ciencias de la Computación e Información
Cognitive Digital Twins
Semantic Web
Industry 4.0
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/12549
Acceder
id CICBA_301a6777878c64ffa4d7bc2c88fd5afe
oai_identifier_str oai:digital.cic.gba.gob.ar:11746/12549
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling What the Semantic Web can do for Cognitive Digital Twins: Challenges and OpportunitiesLezoche, MarioTorres, DiegoCiencias de la Computación e InformaciónCognitive Digital TwinsSemantic WebIndustry 4.0The Cognitive Digital Twin (CDT) is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. Integrating machine learning algorithms and artificial intelligence allows CDTs to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of CDTs are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, semantic web technologies can facilitate advanced analytics and machine learning within CDTs. This article offers a rapid analysis of how Semantic Web approaches can support several aspects of CDT models.2025info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/12549enginfo:eu-repo/semantics/altIdentifier/isbn/978-3-031-91690-8info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-91690-8_18info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:39:59Zoai:digital.cic.gba.gob.ar:11746/12549Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:39:59.574CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
title What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
spellingShingle What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
Lezoche, Mario
Ciencias de la Computación e Información
Cognitive Digital Twins
Semantic Web
Industry 4.0
title_short What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
title_full What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
title_fullStr What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
title_full_unstemmed What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
title_sort What the Semantic Web can do for Cognitive Digital Twins: Challenges and Opportunities
dc.creator.none.fl_str_mv Lezoche, Mario
Torres, Diego
author Lezoche, Mario
author_facet Lezoche, Mario
Torres, Diego
author_role author
author2 Torres, Diego
author2_role author
dc.subject.none.fl_str_mv Ciencias de la Computación e Información
Cognitive Digital Twins
Semantic Web
Industry 4.0
topic Ciencias de la Computación e Información
Cognitive Digital Twins
Semantic Web
Industry 4.0
dc.description.none.fl_txt_mv The Cognitive Digital Twin (CDT) is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. Integrating machine learning algorithms and artificial intelligence allows CDTs to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of CDTs are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, semantic web technologies can facilitate advanced analytics and machine learning within CDTs. This article offers a rapid analysis of how Semantic Web approaches can support several aspects of CDT models.
description The Cognitive Digital Twin (CDT) is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. Integrating machine learning algorithms and artificial intelligence allows CDTs to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of CDTs are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, semantic web technologies can facilitate advanced analytics and machine learning within CDTs. This article offers a rapid analysis of how Semantic Web approaches can support several aspects of CDT models.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/12549
url https://digital.cic.gba.gob.ar/handle/11746/12549
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-3-031-91690-8
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-91690-8_18
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
_version_ 1844618591406653441
score 13.070432

Publicaciones similares