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dc.contributor.authorNogales Moyano, Alberto 
dc.contributor.authorSicilia, Miguel Ángel
dc.contributor.authorGarcía Tejedor, Álvaro José 
dc.date.accessioned2022-10-25T10:37:33Z
dc.date.available2022-10-25T10:37:33Z
dc.date.issued2021
dc.identifier.issn0165-5515spa
dc.identifier.urihttps://hdl.handle.net/10641/3129
dc.description.abstractThe publication of large amounts of open data has become a major trend nowadays. This is a consequence of pro-jects like the Linked Open Data (LOD) community, which publishes and integrates datasets using techniques like Linked Data. Linked Data publishers should follow a set of principles for dataset design. This information is described in a 2011 document that describes tasks as the consideration of reusing vocabularies. With regard to the latter, another project called Linked Open Vocabularies (LOV) attempts to compile the vocabularies used in LOD. These vocabularies have been classified by domain following the subjective criteria of LOV members, which has the inherent risk introducing personal biases. In this paper, we present an automatic classifier of vocabularies based on the main categories of the well-known knowledge source Wikipedia. For this purpose, word-embedding models were used, in combination with Deep Learning techniques. Results show that with a hybrid model of regular Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), vocabularies could be classified with an accuracy of 93.57 per cent. Specifically, 36.25 per cent of the vocabularies belong to the Culture category.spa
dc.language.isoengspa
dc.publisherJournal of Information Sciencespa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectLinked Dataspa
dc.subjectDeep Learningspa
dc.subjectDocument Categorisationspa
dc.titleA domain categorisation of vocabularies based on a deep learning classifier.spa
dc.typejournal articlespa
dc.type.hasVersionSMURspa
dc.rights.accessRightsopen accessspa
dc.description.extent304 KBspa
dc.identifier.doi10.1177/01655515211018170spa
dc.relation.publisherversionhttps://journals.sagepub.com/doi/abs/10.1177/01655515211018170spa


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