Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Virtual Statistics Knowledge Graph Generation from CSV files
Year:2018
Research Areas
  • Information technology and adata processing
Information
Abstract
Statistics data is often published as tabular data by statistics offices and governmental agencies. In last years, many of these institutions have addressed an interoperable way of releasing their data, by means of semantic technologies. Existing approaches normally employ ad-hoc techniques to transform this tabular data to the Statistics Knowledge Graph (SKG) and materialize it. This approach imposes the need of periodical maintenance to ensure the synchronization between the dataset and the transformation result. Using R2RML, the W3C mapping language recommendation, the generation of virtual SKG is possible thanks to the capability of its processors. However, as the size of the R2RML mapping documents depends on the number of columns in the tabular data and the number of dimensions to be generated, it may be prohibitively large, hindering its maintenance. In this paper we propose an approach to reduce the size of the mapping document by extending RMLC, a mapping language for tabular data. We provide a mapping translator from RMLC to R2RML and a comparative analysis over two different real statistics datasets.
International
Si
Congress
6th International Workshop on Semantic Statistics co-located with the 17th International Semantic Web Conference
960
Place
Reviewers
Si
ISBN/ISSN
978-1-61499-894-5
10.3233/978-1-61499-894-5-235
Start Date
08/10/2018
End Date
08/10/2018
From page
235
To page
244
Emerging Topics in Semantic Technologies - {ISWC} 2018 Satellite Events [best papers from 13 of the workshops co-located with the {ISWC} 2018 conference]
Participants
  • Autor: David Chaves Fraga (UPM)
  • Autor: Freddy Priyatna . (UPM)
  • Autor: Idafen Santana Perez (UPM)
  • Autor: Oscar Corcho Garcia (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Ontology Engineering Group
  • Departamento: Inteligencia Artificial
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