Automatic domain-specific learning: towards a methodology for ontology enrichment

[EN] At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, w...

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Detalles Bibliográficos
Autores: Ureña Gómez-Moreno, Pedro, Mestre-Mestre, Eva M.|||0000-0001-5409-2025
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/148357
Acceso en línea:https://riunet.upv.es/handle/10251/148357
Access Level:acceso abierto
Palabra clave:Ontology learning
FunGramKB
Corpus
Terminology
Biology
FILOLOGIA INGLESA
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network_acronym_str ES
network_name_str España
repository_id_str
spelling Automatic domain-specific learning: towards a methodology for ontology enrichmentUreña Gómez-Moreno, PedroMestre-Mestre, Eva M.|||0000-0001-5409-2025Ontology learningFunGramKBCorpusTerminologyBiologyFILOLOGIA INGLESA[EN] At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed ¿ontology enrichment method¿ (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2011-29798-C0201.Serv. Publicaciones de la Universidad de Las Palmas de Gran CanariaDepartamento de Lingüística AplicadaEscuela Politécnica Superior de GandiaGrupo de Análisis de las Lenguas de Especialidad (GALE)Ministerio de Ciencia e InnovaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20172017-12-05journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/148357reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengMinisterio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 FFI2011-29798-C02-01 DESARROLLO DE UN SISTEMA DE REPRESENTACION SEMANTICO-CONCEPTUAL Y SU IMPLEMENTACION EN UN ALGORITMO DE ENLACE BIDIRECCIONAL SINTAXIS-SEMANTICAopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1483572026-06-13T07:49:27Z
dc.title.none.fl_str_mv Automatic domain-specific learning: towards a methodology for ontology enrichment
title Automatic domain-specific learning: towards a methodology for ontology enrichment
spellingShingle Automatic domain-specific learning: towards a methodology for ontology enrichment
Ureña Gómez-Moreno, Pedro
Ontology learning
FunGramKB
Corpus
Terminology
Biology
FILOLOGIA INGLESA
title_short Automatic domain-specific learning: towards a methodology for ontology enrichment
title_full Automatic domain-specific learning: towards a methodology for ontology enrichment
title_fullStr Automatic domain-specific learning: towards a methodology for ontology enrichment
title_full_unstemmed Automatic domain-specific learning: towards a methodology for ontology enrichment
title_sort Automatic domain-specific learning: towards a methodology for ontology enrichment
dc.creator.none.fl_str_mv Ureña Gómez-Moreno, Pedro
Mestre-Mestre, Eva M.|||0000-0001-5409-2025
author Ureña Gómez-Moreno, Pedro
author_facet Ureña Gómez-Moreno, Pedro
Mestre-Mestre, Eva M.|||0000-0001-5409-2025
author_role author
author2 Mestre-Mestre, Eva M.|||0000-0001-5409-2025
author2_role author
dc.contributor.none.fl_str_mv Departamento de Lingüística Aplicada
Escuela Politécnica Superior de Gandia
Grupo de Análisis de las Lenguas de Especialidad (GALE)
Ministerio de Ciencia e Innovación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Ontology learning
FunGramKB
Corpus
Terminology
Biology
FILOLOGIA INGLESA
topic Ontology learning
FunGramKB
Corpus
Terminology
Biology
FILOLOGIA INGLESA
description [EN] At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed ¿ontology enrichment method¿ (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-12-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/148357
url https://riunet.upv.es/handle/10251/148357
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 FFI2011-29798-C02-01 DESARROLLO DE UN SISTEMA DE REPRESENTACION SEMANTICO-CONCEPTUAL Y SU IMPLEMENTACION EN UN ALGORITMO DE ENLACE BIDIRECCIONAL SINTAXIS-SEMANTICA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Serv. Publicaciones de la Universidad de Las Palmas de Gran Canaria
publisher.none.fl_str_mv Serv. Publicaciones de la Universidad de Las Palmas de Gran Canaria
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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