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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22293
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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 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/22293 |
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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) |
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openAccess |
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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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