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A domain categorisation of vocabularies based on a deep learning classifier.

Identificadores del recurso

0165-5515

https://hdl.handle.net/10641/3129

10.1177/01655515211018170

Jatorria

(Repositorio Institucional de la Universidad Francisco de Vitoria)

Fitxa

Izenburu:: A domain categorisation of vocabularies based on a deep learning classifier.
Tema:: Linked Data; Deep Learning; Document Categorisation
Deskribapen:: The 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.; pre-print; 304 KB
Idioma:: English
Harreman:: https://journals.sagepub.com/doi/abs/10.1177/01655515211018170
Autor/Productor:: Nogales Moyano, Alberto; Sicilia, Miguel Ángel; García Tejedor, Álvaro José
Argitaratzaile:: Journal of Information Science
Eskubideak:: Atribución-NoComercial-SinDerivadas 3.0 España; http://creativecommons.org/licenses/by-nc-nd/3.0/es/; openAccess
Data:: 2022-10-25T10:37:33Z; 2021
Tipo de recurso:: article

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