Models to represent Linguistic Linked Data

Bosque-Gil, Julia and Gracia, J. and Montiel-Ponsoda, Elena and Gómez-Pérez, A. (2018). Models to represent Linguistic Linked Data. "Natural Language Engineering", v. 24 (n. 6); pp. 811-859. ISSN 1469-8110.


Title: Models to represent Linguistic Linked Data
  • Bosque-Gil, Julia
  • Gracia, J.
  • Montiel-Ponsoda, Elena
  • Gómez-Pérez, A.
Item Type: Article
Título de Revista/Publicación: Natural Language Engineering
Date: October 2018
ISSN: 1469-8110
Volume: 24
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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, high-lighting their reuse by the community and the modelling challenges that are still to be faced.

Funding Projects

Government of SpainTIN2013- 46238-C4-2-RUnspecifiedUnspecifiedUnspecified
Government of SpainTIN2015-68955-REDTUnspecifiedUnspecifiedUnspecified

More information

Item ID: 72409
DC Identifier:
OAI Identifier:
DOI: 10.1017/S1351324918000347
Official URL:
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 19 Jan 2023 12:40
Last Modified: 19 Jan 2023 12:40
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