Query expansion and noise treatment for information retrieval

Autores
Santos, Emerson L. dos; Avila, Braulio C.; Hasegawa, Fabiano M.; Kaestner, Celso A. A.
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Most of the search engines available over the Web are based on mathematical approaches | classical techniques in the Information Retrieval area. Thereby, they are suitable for the retrieval of documents containing some or all the terms of a query, though not to retrieve the documents containing the meaning those terms were intended to express. This paper presents some advantages obtained from query expansion with WordNet and noise treatment with knowledge on top of Paraconsistent Logic. Both methods are semantically driven, allowing the retrieval of documents which do not contain any term of the original query. Noise treatment results from the combination of a smooth term comparison with knowledge about term authentication based on behaviors of features in the collection. Although query expansion recurs for every query, noise treatment is part of the indexing mechanism, causing no overhead in queries. The domain is retrieval of ontologies represented in Resource Description Framework.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Intelligent agents
Query Expansion
Noise Treatment
Information Retrieval
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22803

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network_name_str SEDICI (UNLP)
spelling Query expansion and noise treatment for information retrievalSantos, Emerson L. dosAvila, Braulio C.Hasegawa, Fabiano M.Kaestner, Celso A. A.Ciencias InformáticasARTIFICIAL INTELLIGENCEIntelligent agentsQuery ExpansionNoise TreatmentInformation RetrievalMost of the search engines available over the Web are based on mathematical approaches | classical techniques in the Information Retrieval area. Thereby, they are suitable for the retrieval of documents containing some or all the terms of a query, though not to retrieve the documents containing the meaning those terms were intended to express. This paper presents some advantages obtained from query expansion with WordNet and noise treatment with knowledge on top of Paraconsistent Logic. Both methods are semantically driven, allowing the retrieval of documents which do not contain any term of the original query. Noise treatment results from the combination of a smooth term comparison with knowledge about term authentication based on behaviors of features in the collection. Although query expansion recurs for every query, noise treatment is part of the indexing mechanism, causing no overhead in queries. The domain is retrieval of ontologies represented in Resource Description Framework.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI)2003-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf717-728http://sedici.unlp.edu.ar/handle/10915/22803enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:11Zoai:sedici.unlp.edu.ar:10915/22803Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:12.232SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Query expansion and noise treatment for information retrieval
title Query expansion and noise treatment for information retrieval
spellingShingle Query expansion and noise treatment for information retrieval
Santos, Emerson L. dos
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Intelligent agents
Query Expansion
Noise Treatment
Information Retrieval
title_short Query expansion and noise treatment for information retrieval
title_full Query expansion and noise treatment for information retrieval
title_fullStr Query expansion and noise treatment for information retrieval
title_full_unstemmed Query expansion and noise treatment for information retrieval
title_sort Query expansion and noise treatment for information retrieval
dc.creator.none.fl_str_mv Santos, Emerson L. dos
Avila, Braulio C.
Hasegawa, Fabiano M.
Kaestner, Celso A. A.
author Santos, Emerson L. dos
author_facet Santos, Emerson L. dos
Avila, Braulio C.
Hasegawa, Fabiano M.
Kaestner, Celso A. A.
author_role author
author2 Avila, Braulio C.
Hasegawa, Fabiano M.
Kaestner, Celso A. A.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Intelligent agents
Query Expansion
Noise Treatment
Information Retrieval
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Intelligent agents
Query Expansion
Noise Treatment
Information Retrieval
dc.description.none.fl_txt_mv Most of the search engines available over the Web are based on mathematical approaches | classical techniques in the Information Retrieval area. Thereby, they are suitable for the retrieval of documents containing some or all the terms of a query, though not to retrieve the documents containing the meaning those terms were intended to express. This paper presents some advantages obtained from query expansion with WordNet and noise treatment with knowledge on top of Paraconsistent Logic. Both methods are semantically driven, allowing the retrieval of documents which do not contain any term of the original query. Noise treatment results from the combination of a smooth term comparison with knowledge about term authentication based on behaviors of features in the collection. Although query expansion recurs for every query, noise treatment is part of the indexing mechanism, causing no overhead in queries. The domain is retrieval of ontologies represented in Resource Description Framework.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description Most of the search engines available over the Web are based on mathematical approaches | classical techniques in the Information Retrieval area. Thereby, they are suitable for the retrieval of documents containing some or all the terms of a query, though not to retrieve the documents containing the meaning those terms were intended to express. This paper presents some advantages obtained from query expansion with WordNet and noise treatment with knowledge on top of Paraconsistent Logic. Both methods are semantically driven, allowing the retrieval of documents which do not contain any term of the original query. Noise treatment results from the combination of a smooth term comparison with knowledge about term authentication based on behaviors of features in the collection. Although query expansion recurs for every query, noise treatment is part of the indexing mechanism, causing no overhead in queries. The domain is retrieval of ontologies represented in Resource Description Framework.
publishDate 2003
dc.date.none.fl_str_mv 2003-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
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format conferenceObject
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22803
url http://sedici.unlp.edu.ar/handle/10915/22803
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
717-728
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