Christophe Debruyne

Davan Chiem Dao, Ghislain Atemezing, Christophe Debruyne

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.

Ademar Crotti Junior, Christophe Debruyne

Knowledge graph construction (KGC) from (semi-)structured data is challenging, and facilitating user involvement is an issue frequently brought up within this community. We cannot deny the progress we have made with respect to (declarative) knowledge graph construction languages and tools to help build such mappings. However, it is surprising that no two studies report on similar protocols. This heterogeneity does not allow for comparing KGC languages, techniques, and tools. This paper first analyses studies involving users to identify the points of comparison. These gaps include a lack of systematic consistency in task design, participant selection, and evaluation metrics. Moreover, there needs to be a systematic way of analyzing the data and reporting the findings, which is also lacking. We thus propose and introduce a user protocol for KGC designed to address this challenge. Where possible, we draw and take elements from the literature we deem fit for such a protocol. The protocol, as such, allows for the comparison of languages and techniques for the RDF Mapping Language (RML) core functionality, which is covered by most of the other state-of-the-art techniques and tools. We also propose how the protocol can be amended to compare extensions (of RML). This protocol provides an important step towards a more comparable evaluation of KGC user studies.