000084323 001__ 84323 000084323 005__ 20201022135733.0 000084323 0247_ $$2doi$$a10.1017/S1351324918000347 000084323 0248_ $$2sideral$$a108649 000084323 037__ $$aART-2018-108649 000084323 041__ $$aeng 000084323 100__ $$aBosque-Gil, J. 000084323 245__ $$aModels to represent linguistic linked data 000084323 260__ $$c2018 000084323 5060_ $$aAccess copy available to the general public$$fUnrestricted 000084323 5203_ $$aAs 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. 000084323 536__ $$9info:eu-repo/grantAgreement/ES/MEC-MINECO/TIN2013-46238-C4-2-R$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2015-68955-REDT 000084323 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000084323 590__ $$a1.13$$b2018 000084323 591__ $$aLINGUISTICS$$b68 / 182 = 0.374$$c2018$$dQ2$$eT2 000084323 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b103 / 133 = 0.774$$c2018$$dQ4$$eT3 000084323 592__ $$a0.315$$b2018 000084323 593__ $$aArtificial Intelligence$$c2018$$dQ2 000084323 593__ $$aSoftware$$c2018$$dQ2 000084323 593__ $$aLinguistics and Language$$c2018$$dQ2 000084323 593__ $$aLanguage and Linguistics$$c2018$$dQ2 000084323 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000084323 700__ $$aGracia, J.$$uUniversidad de Zaragoza 000084323 700__ $$aMontiel-Ponsoda, E. 000084323 700__ $$aGómez-Pérez, A. 000084323 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000084323 773__ $$g24, 6 (2018), 811-859$$pNATURAL LANGUAGE ENGINEERING$$tNATURAL LANGUAGE ENGINEERING$$x1351-3249 000084323 8564_ $$s437171$$uhttps://zaguan.unizar.es/record/84323/files/texto_completo.pdf$$yPostprint 000084323 8564_ $$s71584$$uhttps://zaguan.unizar.es/record/84323/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000084323 909CO $$ooai:zaguan.unizar.es:84323$$particulos$$pdriver 000084323 951__ $$a2020-10-22-13:49:51 000084323 980__ $$aARTICLE