SHACL-DS extends SHACL for RDF dataset validation by introducing declarative targeting of named graphs and graph combinations, but has not yet been demonstrated and assessed on a real, large-scale Knowledge Graph (KG). In this paper, we apply the SHACL-DS approach to validate its use on such a KG. We apply SHACL-DS to the European Railway Infrastructure (ERA RINF) KG, a large-scale RDF dataset in which 56 infrastructure managers contribute data to dedicated named graphs. We migrate the ERA-RINF shapes to SHACL-DS using two strategies and evaluate their performance using a TopBraid SHACL-DS implementation developed for this study. We compare the performance against the SHACL approach, which "flattens" all graphs into a single data graph. Both strategies produce the same results and are faster than the SHACL baseline. Not only do we demonstrate that SHACL-DS is at least as expressive as SHACL, but SHACL-DS also allows the validation scope to be declared inside the shapes artefact, enforces triple provenance through \texttt{GRAPH} clauses, enriches validation reports with per-graph annotations, and enables shape organisation across named shapes graphs.

翻译：SHACL-DS通过引入命名图与图组合的声明式目标定位，扩展了SHACL针对RDF数据集的验证能力，但目前尚未在真实大规模知识图谱（KG）中得到应用与评估。本文首次将SHACL-DS方法应用于此类知识图谱，以验证其实际效用。我们选取欧洲铁路基础设施（ERA RINF）知识图谱作为验证对象——该大规模RDF数据集由56个基础设施管理者通过专属命名图贡献数据组成。采用两种策略将ERA-RINF形状迁移至SHACL-DS，并利用为本研究开发的TopBraid SHACL-DS实现对其性能进行评估。通过与将全量图“扁平化”为单一数据图的SHACL方法进行对比，两种策略均获得相同验证结果且运算速度均优于SHACL基线。我们不仅证明了SHACL-DS至少具有与SHACL同等的表达能力，而且SHACL-DS还允许在形状工件内部声明验证范围，通过\texttt{GRAPH}子句强制执行三元组溯源，在验证报告中补充逐图标注信息，并支持跨命名形状图的形状编排。