A domain categorisation of vocabularies based on a deep learning classifier. (June 2023)

• Record Type: Journal Article
• Authors: Nogales, Alberto; Sicilia, Miguel-Angel; García-Tejedor, Álvaro J
• Is Part Of: Journal of information science. Volume 49:Number 3(2023)
• Page Start: 699
• Page End: 710
• Publication Date: 2023-06
• Subjects: Deep learning -- document categorisation -- linked data -- ontologies; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/01655515211018170
• Languages: English
• ISSNs: 0165-5515

Abstract

The publication of large amounts of open data is an increasing trend. This is a consequence of initiatives like Linked Open Data (LOD) that aims at publishing and linking data sets published in the World Wide Web. Linked Data publishers should follow a set of principles for their task. This information is described in a 2011 document that includes the consideration of reusing vocabularies as key. The Linked Open Vocabularies (LOV) project attempts to collect the vocabularies and ontologies commonly used in LOD. These ontologies have been classified by domain following the criteria of LOV members, thus having the disadvantage of introducing personal biases. This article presents an automatic classifier of ontologies based on the main categories appearing in Wikipedia. For that purpose, word-embedding models are used in combination with deep learning techniques. Results show that with a hybrid model of regular Deep Neural Networks (DNNs), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), classification could be made with an accuracy of 93.57%. A further evaluation of the domain matchings between LOV and the classifier brings possible matchings in 79.8% of the cases.