Improving Inconsistency Resolution by Considering Global Conflicts

Autores
Deagustini, Cristhian Ariel David; Martinez, Maria Vanina; Falappa, Marcelo Alejandro; Simari, Guillermo Ricardo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Over the years, inconsistency management has caught the attention of researchers of different areas. Inconsistency is a problem that arises in many different scenarios, for instance, ontology development or knowledge integration. In such settings, it is important to have adequate automatic tools for handling potential conflicts. Here we propose a novel approach to belief base consolidation based on a refinement of kernel contraction that accounts for the relation among kernels using clusters. We define cluster contraction based consolidation operators as the contraction by falsum on a belief base using cluster incision functions, a refinement of (smooth) kernel incision functions. A cluster contraction-based approach to belief bases consolidation can successfully obtain a belief base satisfying the expected consistency requirement. Also, we show that the application of cluster contraction-based consolidation operators satisfy minimality regarding loss of information and are equivalent to operators based on maxichoice contraction.
Fil: Deagustini, Cristhian Ariel David. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Maria Vanina. University of Oxford; Reino Unido
Fil: Falappa, Marcelo Alejandro. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Simari, Guillermo Ricardo. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Inconsistency Management
Belief Consolidation
Minimal Loss of Information
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/12630
Acceder
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spelling Improving Inconsistency Resolution by Considering Global ConflictsDeagustini, Cristhian Ariel DavidMartinez, Maria VaninaFalappa, Marcelo AlejandroSimari, Guillermo RicardoInconsistency ManagementBelief ConsolidationMinimal Loss of Informationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Over the years, inconsistency management has caught the attention of researchers of different areas. Inconsistency is a problem that arises in many different scenarios, for instance, ontology development or knowledge integration. In such settings, it is important to have adequate automatic tools for handling potential conflicts. Here we propose a novel approach to belief base consolidation based on a refinement of kernel contraction that accounts for the relation among kernels using clusters. We define cluster contraction based consolidation operators as the contraction by falsum on a belief base using cluster incision functions, a refinement of (smooth) kernel incision functions. A cluster contraction-based approach to belief bases consolidation can successfully obtain a belief base satisfying the expected consistency requirement. Also, we show that the application of cluster contraction-based consolidation operators satisfy minimality regarding loss of information and are equivalent to operators based on maxichoice contraction.Fil: Deagustini, Cristhian Ariel David. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martinez, Maria Vanina. University of Oxford; Reino UnidoFil: Falappa, Marcelo Alejandro. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Simari, Guillermo Ricardo. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaSpringer2014-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/12630Deagustini, Cristhian Ariel David; Martinez, Maria Vanina; Falappa, Marcelo Alejandro; Simari, Guillermo Ricardo; Improving Inconsistency Resolution by Considering Global Conflicts; Springer; Lecture Notes In Computer Science; 8720; 7-2014; 120-1330302-9743enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/chapter/10.1007/978-3-319-11508-5_11info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-11508-5_11info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:36:47Zoai:ri.conicet.gov.ar:11336/12630instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:36:48.064CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improving Inconsistency Resolution by Considering Global Conflicts
title Improving Inconsistency Resolution by Considering Global Conflicts
spellingShingle Improving Inconsistency Resolution by Considering Global Conflicts
Deagustini, Cristhian Ariel David
Inconsistency Management
Belief Consolidation
Minimal Loss of Information
title_short Improving Inconsistency Resolution by Considering Global Conflicts
title_full Improving Inconsistency Resolution by Considering Global Conflicts
title_fullStr Improving Inconsistency Resolution by Considering Global Conflicts
title_full_unstemmed Improving Inconsistency Resolution by Considering Global Conflicts
title_sort Improving Inconsistency Resolution by Considering Global Conflicts
dc.creator.none.fl_str_mv Deagustini, Cristhian Ariel David
Martinez, Maria Vanina
Falappa, Marcelo Alejandro
Simari, Guillermo Ricardo
author Deagustini, Cristhian Ariel David
author_facet Deagustini, Cristhian Ariel David
Martinez, Maria Vanina
Falappa, Marcelo Alejandro
Simari, Guillermo Ricardo
author_role author
author2 Martinez, Maria Vanina
Falappa, Marcelo Alejandro
Simari, Guillermo Ricardo
author2_role author
author
author
dc.subject.none.fl_str_mv Inconsistency Management
Belief Consolidation
Minimal Loss of Information
topic Inconsistency Management
Belief Consolidation
Minimal Loss of Information
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Over the years, inconsistency management has caught the attention of researchers of different areas. Inconsistency is a problem that arises in many different scenarios, for instance, ontology development or knowledge integration. In such settings, it is important to have adequate automatic tools for handling potential conflicts. Here we propose a novel approach to belief base consolidation based on a refinement of kernel contraction that accounts for the relation among kernels using clusters. We define cluster contraction based consolidation operators as the contraction by falsum on a belief base using cluster incision functions, a refinement of (smooth) kernel incision functions. A cluster contraction-based approach to belief bases consolidation can successfully obtain a belief base satisfying the expected consistency requirement. Also, we show that the application of cluster contraction-based consolidation operators satisfy minimality regarding loss of information and are equivalent to operators based on maxichoice contraction.
Fil: Deagustini, Cristhian Ariel David. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Maria Vanina. University of Oxford; Reino Unido
Fil: Falappa, Marcelo Alejandro. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Simari, Guillermo Ricardo. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Over the years, inconsistency management has caught the attention of researchers of different areas. Inconsistency is a problem that arises in many different scenarios, for instance, ontology development or knowledge integration. In such settings, it is important to have adequate automatic tools for handling potential conflicts. Here we propose a novel approach to belief base consolidation based on a refinement of kernel contraction that accounts for the relation among kernels using clusters. We define cluster contraction based consolidation operators as the contraction by falsum on a belief base using cluster incision functions, a refinement of (smooth) kernel incision functions. A cluster contraction-based approach to belief bases consolidation can successfully obtain a belief base satisfying the expected consistency requirement. Also, we show that the application of cluster contraction-based consolidation operators satisfy minimality regarding loss of information and are equivalent to operators based on maxichoice contraction.
publishDate 2014
dc.date.none.fl_str_mv 2014-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/12630
Deagustini, Cristhian Ariel David; Martinez, Maria Vanina; Falappa, Marcelo Alejandro; Simari, Guillermo Ricardo; Improving Inconsistency Resolution by Considering Global Conflicts; Springer; Lecture Notes In Computer Science; 8720; 7-2014; 120-133
0302-9743
url http://hdl.handle.net/11336/12630
identifier_str_mv Deagustini, Cristhian Ariel David; Martinez, Maria Vanina; Falappa, Marcelo Alejandro; Simari, Guillermo Ricardo; Improving Inconsistency Resolution by Considering Global Conflicts; Springer; Lecture Notes In Computer Science; 8720; 7-2014; 120-133
0302-9743
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/chapter/10.1007/978-3-319-11508-5_11
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-11508-5_11
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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