Abstract
Most data available on the Web do not conform to the RDF data model. A number of tools/approaches have been developed to encourage the transition to RDF. Manual and automatic tools/approaches tend to be complex and rigid. On the other hand, semi-automatic tools can hide and automate complex tasks while enhancing flexibility by solicitating human experts for decision making purposes. In this paper, we describe a semi-automatic approach to facilitate the transformation of heterogeneous semi-structured data to RDF. The originality of our approach is its ability to generate exhaustive descriptions using entities from several ontologies without requiring end-users to have a knowledge of ontologies. We provide an implementation of our approach and demonstrate its use using a real dataset from an open data portal.
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Notes
- 1.
http://data.metropolegrenoble.fr/ckan/dataset/parkings-de-grenoble/resource/a6919f90-4c38-4ee0-a4ec-403db77f5a4b, last accessed on 7 December 2019.
- 2.
http://data.metropolegrenoble.fr/, last accessed on 7 December 2019.
- 3.
https://www.mobivoc.org/, last accessed 10 February 2020.
- 4.
https://schema.org/, last accessed 10 February 2020.
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https://www.w3.org/2003/01/geo/, last accessed 10 February 2020.
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https://www.dublincore.org/specifications/dublin-core/dcmi-terms/, last accessed 10 February 2020.
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Bakerally, N. et al. (2020). Semi-automatic RDFization Using Automatically Generated Mappings. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_5
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