Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Models to Represent Linguistic Linked Data
Año:2018
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
As the interest of the Semantic Web and computational linguistics communities in linguistic linked data (LLD) keeps increasing and the number of contributions that dwell on LLD rapidly grows, scholars (and linguists in particular) interested in the development of LLD resources sometimes find it difficult to determine which mechanism is suitable for their needs and which challenges have already been addressed. This review seeks to present the state of the art on the models, ontologies and their extensions to represent language resources as LLD by focusing on the nature of the linguistic content they aim to encode. Four basic groups of models are distinguished in this work: models to represent the main elements of lexical resources (group 1), vocabularies developed as extensions to models in group 1 and ontologies that provide more granularity on specific levels of linguistic analysis (group 2), catalogues of linguistic data categories (group 3) and other models such as corpora models or service-oriented ones (group 4). Contributions encompassed in these four groups are described, highlighting their reuse by the community and the modelling challenges that are still to be faced.
Internacional
Si
JCR del ISI
Si
Título de la revista
Natural Language Engineering
ISSN
1351-3249
Factor de impacto JCR
0,8
Información de impacto
Datos JCR del año 2017, Q3 (mitad inferior)
Volumen
24
DOI
10.1017/S1351324918000347
Número de revista
6
Desde la página
811
Hasta la página
859
Mes
NOVIEMBRE
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Julia Bosque Gil (UPM)
  • Autor: Jorge Gracia Del Rio (UPM)
  • Autor: Elena Montiel Ponsoda (UPM)
  • Autor: Asuncion de Maria Gomez Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
  • Departamento: Lingüística Aplicada a la Ciencia y a la Tecnología
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