Conference Papers Year : 2023

A Framework to Assess Knowledge Graphs Accountability

Abstract

Knowledge Graphs (KGs), and Linked Open Data in particular, enable the generation and exchange of more and more information on the Web. In order to use and reuse these data properly, the presence of accountability information is essential. Accountability requires specific and accurate information about people's responsibilities and actions. In this article, we define KGAcc, a framework dedicated to the assessment of RDF graphs accountability. It consists of accountability requirements and a measure of accountability for KGs. Then, we evaluate KGs from the LOD cloud and describe the results obtained. Finally, we compare our approach with data quality and FAIR assessment frameworks to highlight the differences.
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hal-04372234 , version 1 (04-01-2024)

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Jennie Andersen, Sylvie Cazalens, Philippe Lamarre, Pierre Maillot. A Framework to Assess Knowledge Graphs Accountability. WI-IAT - 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Oct 2023, Venice, Italy. pp.213-220, ⟨10.1109/WI-IAT59888.2023.00034⟩. ⟨hal-04372234⟩
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