Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria

Autores
Cecchini, Rocío Luján; Lorenzetti, Carlos Martin; Maguitman, Ana Gabriela
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we propose techniques based on single - and multi-objective evolutionary algorithms to automatically evolve a population of topical queries. The developed techniques can be applied in the implementation of a topical search system. We report on the results of different strategies that attempt to evolve conjunctive and disjunctive queries. Our analysis reveals the limitations of the single-objective approach and highlights the advantages of applying multi-objective evolutionary algorithms for the problem at hand. In addition, we observe that disjunctive queries have the potential to achieve better retrieval performance than conjunctive queries. Finally, we show that the multi-objective evolutionary approach results in better performance than a baseline and other state-of-the-art techniques for query refinement.
Fil: Cecchini, Rocío Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina
Fil: Lorenzetti, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Fil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Materia
CONJUNCTIVE QUERIES
DISJUNCTIVE QUERIES
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
TOPICAL SEARCH
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/75523
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteriaCecchini, Rocío LujánLorenzetti, Carlos MartinMaguitman, Ana GabrielaCONJUNCTIVE QUERIESDISJUNCTIVE QUERIESMULTI-OBJECTIVE EVOLUTIONARY ALGORITHMSTOPICAL SEARCHhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this work we propose techniques based on single - and multi-objective evolutionary algorithms to automatically evolve a population of topical queries. The developed techniques can be applied in the implementation of a topical search system. We report on the results of different strategies that attempt to evolve conjunctive and disjunctive queries. Our analysis reveals the limitations of the single-objective approach and highlights the advantages of applying multi-objective evolutionary algorithms for the problem at hand. In addition, we observe that disjunctive queries have the potential to achieve better retrieval performance than conjunctive queries. Finally, we show that the multi-objective evolutionary approach results in better performance than a baseline and other state-of-the-art techniques for query refinement.Fil: Cecchini, Rocío Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; ArgentinaFil: Lorenzetti, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; ArgentinaSociedad Iberoamericana de Inteligencia Artificial2009-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/75523Cecchini, Rocío Luján; Lorenzetti, Carlos Martin; Maguitman, Ana Gabriela; Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 13; 44; 2-2009; 14-261137-36011988-3064CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.redalyc.org/html/925/92513154003/index.htmlinfo:eu-repo/semantics/altIdentifier/url/http://journaldocs.iberamia.org/articles/620/article%20(1).pdfinfo:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/Vol.1-14.html#2009info:eu-repo/semantics/altIdentifier/doi/10.4114/ia.v13i44.1042info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:01:16Zoai:ri.conicet.gov.ar:11336/75523instacron: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:34982025-09-29 10:01:17.19CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
title Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
spellingShingle Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
Cecchini, Rocío Luján
CONJUNCTIVE QUERIES
DISJUNCTIVE QUERIES
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
TOPICAL SEARCH
title_short Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
title_full Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
title_fullStr Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
title_full_unstemmed Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
title_sort Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria
dc.creator.none.fl_str_mv Cecchini, Rocío Luján
Lorenzetti, Carlos Martin
Maguitman, Ana Gabriela
author Cecchini, Rocío Luján
author_facet Cecchini, Rocío Luján
Lorenzetti, Carlos Martin
Maguitman, Ana Gabriela
author_role author
author2 Lorenzetti, Carlos Martin
Maguitman, Ana Gabriela
author2_role author
author
dc.subject.none.fl_str_mv CONJUNCTIVE QUERIES
DISJUNCTIVE QUERIES
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
TOPICAL SEARCH
topic CONJUNCTIVE QUERIES
DISJUNCTIVE QUERIES
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
TOPICAL SEARCH
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work we propose techniques based on single - and multi-objective evolutionary algorithms to automatically evolve a population of topical queries. The developed techniques can be applied in the implementation of a topical search system. We report on the results of different strategies that attempt to evolve conjunctive and disjunctive queries. Our analysis reveals the limitations of the single-objective approach and highlights the advantages of applying multi-objective evolutionary algorithms for the problem at hand. In addition, we observe that disjunctive queries have the potential to achieve better retrieval performance than conjunctive queries. Finally, we show that the multi-objective evolutionary approach results in better performance than a baseline and other state-of-the-art techniques for query refinement.
Fil: Cecchini, Rocío Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina
Fil: Lorenzetti, Carlos Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
Fil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina
description In this work we propose techniques based on single - and multi-objective evolutionary algorithms to automatically evolve a population of topical queries. The developed techniques can be applied in the implementation of a topical search system. We report on the results of different strategies that attempt to evolve conjunctive and disjunctive queries. Our analysis reveals the limitations of the single-objective approach and highlights the advantages of applying multi-objective evolutionary algorithms for the problem at hand. In addition, we observe that disjunctive queries have the potential to achieve better retrieval performance than conjunctive queries. Finally, we show that the multi-objective evolutionary approach results in better performance than a baseline and other state-of-the-art techniques for query refinement.
publishDate 2009
dc.date.none.fl_str_mv 2009-02
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/11336/75523
Cecchini, Rocío Luján; Lorenzetti, Carlos Martin; Maguitman, Ana Gabriela; Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 13; 44; 2-2009; 14-26
1137-3601
1988-3064
CONICET Digital
CONICET
url http://hdl.handle.net/11336/75523
identifier_str_mv Cecchini, Rocío Luján; Lorenzetti, Carlos Martin; Maguitman, Ana Gabriela; Evolving disjunctive and conjunctive topical queries based on multi-objective optimization criteria; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 13; 44; 2-2009; 14-26
1137-3601
1988-3064
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.redalyc.org/html/925/92513154003/index.html
info:eu-repo/semantics/altIdentifier/url/http://journaldocs.iberamia.org/articles/620/article%20(1).pdf
info:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/Vol.1-14.html#2009
info:eu-repo/semantics/altIdentifier/doi/10.4114/ia.v13i44.1042
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
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 13.069144

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