Relational databases as a massive information source for defeasible argumentation

Autores
Deagustini, Cristhian Ariel David; Fulladoza Dalibón, Santiago Emanuel; Gottifredi, Sebastián; Falappa, Marcelo Alejandro; Chesñevar, Carlos Iván; Simari, Guillermo Ricardo
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Argumentation provides a sophisticated yet powerful mechanism for the formalization of commonsense reasoning in knowledge-based systems, with application in many areas of Artificial Intelligence. Nowadays, most argumentation systems build their arguments on the basis of a single, fixed knowledge base, often under the form of a logic program as in Defeasible Logic Programming or in Assumption-Based Argumentation. Currently, adding new information to such programs requires a manual encoding, which is not feasible for many real-world environments which involve large amounts of data, usually conceptualized as relational databases. This paper presents a novel approach to compute arguments from premises obtained from relational databases, identifying several relevant aspects. In our setting, different databases can be updated by external, independent applications, leading to changes in the spectrum of available arguments. We present algorithms for integrating a database management system with an argument-based inference engine. Empirical results and running-time analysis associated with our approach show that it provides a powerful alternative for efficiently achieving massive argumentation, taking advantage of modern DBMS technologies. We contend that our proposal is significant for developing new architectures for knowledge-based applications, such as Decision Support Systems and Recommender Systems, using argumentation as the underlying inference model.
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. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fulladoza Dalibón, Santiago Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; Argentina
Fil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
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: Chesñevar, Carlos Iván. 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
Argument-Supporting Data Retrieval
Defeasible Argumentation
Knowledge-Based Systems
Massive Argumentation
Relational Databases
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:11336/79056
Acceder
id CONICETDig_4141ba9e425505d74d180f306cc45f10
oai_identifier_str oai:ri.conicet.gov.ar:11336/79056
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Relational databases as a massive information source for defeasible argumentationDeagustini, Cristhian Ariel DavidFulladoza Dalibón, Santiago EmanuelGottifredi, SebastiánFalappa, Marcelo AlejandroChesñevar, Carlos IvánSimari, Guillermo RicardoArgument-Supporting Data RetrievalDefeasible ArgumentationKnowledge-Based SystemsMassive ArgumentationRelational Databaseshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Argumentation provides a sophisticated yet powerful mechanism for the formalization of commonsense reasoning in knowledge-based systems, with application in many areas of Artificial Intelligence. Nowadays, most argumentation systems build their arguments on the basis of a single, fixed knowledge base, often under the form of a logic program as in Defeasible Logic Programming or in Assumption-Based Argumentation. Currently, adding new information to such programs requires a manual encoding, which is not feasible for many real-world environments which involve large amounts of data, usually conceptualized as relational databases. This paper presents a novel approach to compute arguments from premises obtained from relational databases, identifying several relevant aspects. In our setting, different databases can be updated by external, independent applications, leading to changes in the spectrum of available arguments. We present algorithms for integrating a database management system with an argument-based inference engine. Empirical results and running-time analysis associated with our approach show that it provides a powerful alternative for efficiently achieving massive argumentation, taking advantage of modern DBMS technologies. We contend that our proposal is significant for developing new architectures for knowledge-based applications, such as Decision Support Systems and Recommender Systems, using argumentation as the underlying inference model.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. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fulladoza Dalibón, Santiago Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; ArgentinaFil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: 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: Chesñevar, Carlos Iván. 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; ArgentinaElsevier Science2013-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/79056Deagustini, Cristhian Ariel David; Fulladoza Dalibón, Santiago Emanuel; Gottifredi, Sebastián; Falappa, Marcelo Alejandro; Chesñevar, Carlos Iván; et al.; Relational databases as a massive information source for defeasible argumentation; Elsevier Science; Knowledge-Based Systems; 51; 10-2013; 93-1090950-7051CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0950705113002128info:eu-repo/semantics/altIdentifier/doi/10.1016/j.knosys.2013.07.010info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T11:48:31Zoai:ri.conicet.gov.ar:11336/79056instacron: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:34982025-09-17 11:48:31.347CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Relational databases as a massive information source for defeasible argumentation
title Relational databases as a massive information source for defeasible argumentation
spellingShingle Relational databases as a massive information source for defeasible argumentation
Deagustini, Cristhian Ariel David
Argument-Supporting Data Retrieval
Defeasible Argumentation
Knowledge-Based Systems
Massive Argumentation
Relational Databases
title_short Relational databases as a massive information source for defeasible argumentation
title_full Relational databases as a massive information source for defeasible argumentation
title_fullStr Relational databases as a massive information source for defeasible argumentation
title_full_unstemmed Relational databases as a massive information source for defeasible argumentation
title_sort Relational databases as a massive information source for defeasible argumentation
dc.creator.none.fl_str_mv Deagustini, Cristhian Ariel David
Fulladoza Dalibón, Santiago Emanuel
Gottifredi, Sebastián
Falappa, Marcelo Alejandro
Chesñevar, Carlos Iván
Simari, Guillermo Ricardo
author Deagustini, Cristhian Ariel David
author_facet Deagustini, Cristhian Ariel David
Fulladoza Dalibón, Santiago Emanuel
Gottifredi, Sebastián
Falappa, Marcelo Alejandro
Chesñevar, Carlos Iván
Simari, Guillermo Ricardo
author_role author
author2 Fulladoza Dalibón, Santiago Emanuel
Gottifredi, Sebastián
Falappa, Marcelo Alejandro
Chesñevar, Carlos Iván
Simari, Guillermo Ricardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Argument-Supporting Data Retrieval
Defeasible Argumentation
Knowledge-Based Systems
Massive Argumentation
Relational Databases
topic Argument-Supporting Data Retrieval
Defeasible Argumentation
Knowledge-Based Systems
Massive Argumentation
Relational Databases
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Argumentation provides a sophisticated yet powerful mechanism for the formalization of commonsense reasoning in knowledge-based systems, with application in many areas of Artificial Intelligence. Nowadays, most argumentation systems build their arguments on the basis of a single, fixed knowledge base, often under the form of a logic program as in Defeasible Logic Programming or in Assumption-Based Argumentation. Currently, adding new information to such programs requires a manual encoding, which is not feasible for many real-world environments which involve large amounts of data, usually conceptualized as relational databases. This paper presents a novel approach to compute arguments from premises obtained from relational databases, identifying several relevant aspects. In our setting, different databases can be updated by external, independent applications, leading to changes in the spectrum of available arguments. We present algorithms for integrating a database management system with an argument-based inference engine. Empirical results and running-time analysis associated with our approach show that it provides a powerful alternative for efficiently achieving massive argumentation, taking advantage of modern DBMS technologies. We contend that our proposal is significant for developing new architectures for knowledge-based applications, such as Decision Support Systems and Recommender Systems, using argumentation as the underlying inference model.
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. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fulladoza Dalibón, Santiago Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias de la Administración; Argentina
Fil: Gottifredi, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
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: Chesñevar, Carlos Iván. 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 Argumentation provides a sophisticated yet powerful mechanism for the formalization of commonsense reasoning in knowledge-based systems, with application in many areas of Artificial Intelligence. Nowadays, most argumentation systems build their arguments on the basis of a single, fixed knowledge base, often under the form of a logic program as in Defeasible Logic Programming or in Assumption-Based Argumentation. Currently, adding new information to such programs requires a manual encoding, which is not feasible for many real-world environments which involve large amounts of data, usually conceptualized as relational databases. This paper presents a novel approach to compute arguments from premises obtained from relational databases, identifying several relevant aspects. In our setting, different databases can be updated by external, independent applications, leading to changes in the spectrum of available arguments. We present algorithms for integrating a database management system with an argument-based inference engine. Empirical results and running-time analysis associated with our approach show that it provides a powerful alternative for efficiently achieving massive argumentation, taking advantage of modern DBMS technologies. We contend that our proposal is significant for developing new architectures for knowledge-based applications, such as Decision Support Systems and Recommender Systems, using argumentation as the underlying inference model.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
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/11336/79056
Deagustini, Cristhian Ariel David; Fulladoza Dalibón, Santiago Emanuel; Gottifredi, Sebastián; Falappa, Marcelo Alejandro; Chesñevar, Carlos Iván; et al.; Relational databases as a massive information source for defeasible argumentation; Elsevier Science; Knowledge-Based Systems; 51; 10-2013; 93-109
0950-7051
CONICET Digital
CONICET
url http://hdl.handle.net/11336/79056
identifier_str_mv Deagustini, Cristhian Ariel David; Fulladoza Dalibón, Santiago Emanuel; Gottifredi, Sebastián; Falappa, Marcelo Alejandro; Chesñevar, Carlos Iván; et al.; Relational databases as a massive information source for defeasible argumentation; Elsevier Science; Knowledge-Based Systems; 51; 10-2013; 93-109
0950-7051
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0950705113002128
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.knosys.2013.07.010
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
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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
_version_ 1843606833821908992
score 13.001348

Publicaciones similares