ISWC 2014, 5th International Workshop on Consuming Linked Data (COLD 2014), 20 October 2014, Riva del Garda, Italy
Datasets published in the LOD cloud are recommended to follow some best practice in order to be 4-5 stars Linked Data compliant. They can often be consumed and accessed by di erent means such as API access, bulk download or as linked data fragments, but most of the time, a SPARQL endpoint is also provided. While the LOD cloud keeps growing, having a quick glimpse of those datasets is getting harder and there is a need to develop new methods enabling to detect automatically what an arbitrary dataset is about and to recommend visualizations for data samples. We consider that \a visualization is worth a million triples", and in this paper, we propose a novel approach that mines the content of datasets and automatically generates visualizations. Our approach is directly based on the usage of SPARQL queries that will detect the important categories of a dataset and that will speci cally consider the properties used by the objects which have been interlinked via owl:sameAs links. We then propose to associate type of visualization for those categories. We have implemented this approach into a so-called
Linked Data Vizualization Wizard (LDVizWiz).
Type:
Conference
City:
Riva Del Garda
Date:
2014-10-20
Department:
Data Science
Eurecom Ref:
4380
Copyright:
© Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in ISWC 2014, 5th International Workshop on Consuming Linked Data (COLD 2014), 20 October 2014, Riva del Garda, Italy and is available at :
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