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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2605.10540 |
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| _version_ | 1866918494584438784 |
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| author | Dao, Davan Chiem Atemezing, Ghislain Debruyne, Christophe |
| author_facet | Dao, Davan Chiem Atemezing, Ghislain Debruyne, Christophe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10540 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Keeping track of errors: A study of SHACL-DS for RDF dataset validation on the ERA RINF Knowledge Graph Dao, Davan Chiem Atemezing, Ghislain Debruyne, Christophe Databases 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. |
| title | Keeping track of errors: A study of SHACL-DS for RDF dataset validation on the ERA RINF Knowledge Graph |
| topic | Databases |
| url | https://arxiv.org/abs/2605.10540 |