Taking advantages of ontology and contexts to determine similarity of data

Autores
Buccella, Agustina; Cechich, Alejandra; Rodríguez Brisaboa, Nieves
Año de publicación
2004
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Data integration is the process of unifying data sharing some common semantics but are originated from unrelated sources. In our work we consider these sources are autonomous, heterogeneous and they are physically distributed. These three characteristics make the integration task more difficult as there are several aspects to bear in mind. In this work we only focus on one of these aspects, the semantic heterogeneity, which deals with the meaning of the concepts within the information sources. As each source contains a specific vocabulary according to its understanding of the world, terms denoting same meaning can be very difficult to find. In this paper we will briefly explain our method to find similarities using ontologies and contexts. We will propose some improvements in the similarity functions in order to take advantages of the information the ontologies provide.
Eje: I - Workshop de Ingeniería de Software y Base de Datos
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Semantic Heterogeneity
Ontology
Context
Similarity
SOFTWARE ENGINEERING
base de datos
Semantics
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:10915/22293

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network_name_str SEDICI (UNLP)
spelling Taking advantages of ontology and contexts to determine similarity of dataBuccella, AgustinaCechich, AlejandraRodríguez Brisaboa, NievesCiencias InformáticasSemantic HeterogeneityOntologyContextSimilaritySOFTWARE ENGINEERINGbase de datosSemanticsData integration is the process of unifying data sharing some common semantics but are originated from unrelated sources. In our work we consider these sources are autonomous, heterogeneous and they are physically distributed. These three characteristics make the integration task more difficult as there are several aspects to bear in mind. In this work we only focus on one of these aspects, the semantic heterogeneity, which deals with the meaning of the concepts within the information sources. As each source contains a specific vocabulary according to its understanding of the world, terms denoting same meaning can be very difficult to find. In this paper we will briefly explain our method to find similarities using ontologies and contexts. We will propose some improvements in the similarity functions in order to take advantages of the information the ontologies provide.Eje: I - Workshop de Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI)2004info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22293enginfo: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:UNLP2025-10-15T10:47:37Zoai:sedici.unlp.edu.ar:10915/22293Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:37.602SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Taking advantages of ontology and contexts to determine similarity of data
title Taking advantages of ontology and contexts to determine similarity of data
spellingShingle Taking advantages of ontology and contexts to determine similarity of data
Buccella, Agustina
Ciencias Informáticas
Semantic Heterogeneity
Ontology
Context
Similarity
SOFTWARE ENGINEERING
base de datos
Semantics
title_short Taking advantages of ontology and contexts to determine similarity of data
title_full Taking advantages of ontology and contexts to determine similarity of data
title_fullStr Taking advantages of ontology and contexts to determine similarity of data
title_full_unstemmed Taking advantages of ontology and contexts to determine similarity of data
title_sort Taking advantages of ontology and contexts to determine similarity of data
dc.creator.none.fl_str_mv Buccella, Agustina
Cechich, Alejandra
Rodríguez Brisaboa, Nieves
author Buccella, Agustina
author_facet Buccella, Agustina
Cechich, Alejandra
Rodríguez Brisaboa, Nieves
author_role author
author2 Cechich, Alejandra
Rodríguez Brisaboa, Nieves
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Semantic Heterogeneity
Ontology
Context
Similarity
SOFTWARE ENGINEERING
base de datos
Semantics
topic Ciencias Informáticas
Semantic Heterogeneity
Ontology
Context
Similarity
SOFTWARE ENGINEERING
base de datos
Semantics
dc.description.none.fl_txt_mv Data integration is the process of unifying data sharing some common semantics but are originated from unrelated sources. In our work we consider these sources are autonomous, heterogeneous and they are physically distributed. These three characteristics make the integration task more difficult as there are several aspects to bear in mind. In this work we only focus on one of these aspects, the semantic heterogeneity, which deals with the meaning of the concepts within the information sources. As each source contains a specific vocabulary according to its understanding of the world, terms denoting same meaning can be very difficult to find. In this paper we will briefly explain our method to find similarities using ontologies and contexts. We will propose some improvements in the similarity functions in order to take advantages of the information the ontologies provide.
Eje: I - Workshop de Ingeniería de Software y Base de Datos
Red de Universidades con Carreras en Informática (RedUNCI)
description Data integration is the process of unifying data sharing some common semantics but are originated from unrelated sources. In our work we consider these sources are autonomous, heterogeneous and they are physically distributed. These three characteristics make the integration task more difficult as there are several aspects to bear in mind. In this work we only focus on one of these aspects, the semantic heterogeneity, which deals with the meaning of the concepts within the information sources. As each source contains a specific vocabulary according to its understanding of the world, terms denoting same meaning can be very difficult to find. In this paper we will briefly explain our method to find similarities using ontologies and contexts. We will propose some improvements in the similarity functions in order to take advantages of the information the ontologies provide.
publishDate 2004
dc.date.none.fl_str_mv 2004
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info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
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dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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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)
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