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Conference Papers Year : 2024

An Open Platform for Quality Measures in a Linked Data Index

Abstract

There is a great diversity of RDF datasets publicly available on the web. Choosing among them requires assessing their “fitness for use” for a particular use case, and thus, finding the right quality measures and evaluating data sources according to them. However, this is not an easy task due to the large number of possible quality measures, and the multiplicity of implementation and assessment platforms. Therefore, there is a need for a common way to define measures and evaluate RDF datasets, using open standards and tools. IndeGx is a SPARQL-based framework to design indexes of Knowledge Graphs declaratively. We extend it to support more advanced data quality measures. We demonstrate our approach by reproducing two existing measures, showing how one can formalize and add measures using such an open declarative framework.
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hal-04575211 , version 1 (14-05-2024)

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Pierre Maillot, Jennie Andersen, Sylvie Cazalens, Catherine Faron, Fabien Gandon, et al.. An Open Platform for Quality Measures in a Linked Data Index. WWW '24: The ACM Web Conference 2024, May 2024, Singapore, Singapore. pp.1087-1090, ⟨10.1145/3589335.3651443⟩. ⟨hal-04575211⟩
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