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.

翻译：知识图谱（KG），特别是关联开放数据，促进了网络上越来越多信息的生成与交换。为了正确使用和重用这些数据，可问责性信息的存在至关重要。可问责性要求提供关于人员责任与行为的特定且准确的信息。本文定义了KGAcc——一个专门用于评估RDF图可问责性的框架，包含可问责性需求及知识图谱可问责性的度量。随后，我们评估了来自LOD云的知识图谱，并描述了所获得的结果。最后，我们将我们的方法与数据质量和FAIR评估框架进行比较，以突出差异。