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.
Keywords
Cite
@article{arxiv.2309.16285,
title = {A Framework to Assess Knowledge Graphs Accountability},
author = {Jennie Andersen and Sylvie Cazalens and Philippe Lamarre and Pierre Maillot},
journal= {arXiv preprint arXiv:2309.16285},
year = {2023}
}
Comments
8 pages, to be published in: 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)