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Validating knowledge graphs (KGs) ensures their quality and reliability in real-world applications. The Shapes Constraint Language (SHACL) has emerged as a recommended language for validating RDF KGs, by defining structured constraints. Many organizations leverage SHACL validation and its reports to detect problems, guide corrections, and improve data quality. Yet, large-scale KGs often produce extensive validation reports, making manual analysis infeasible. To address this challenge, we present the SHACL Dashboard, a novel online tool for visualization and multidimensional analysis of SHACL validation reports. SHACL Dashboard provides analytical plots, and fine-grained insights into individual constraints. These functionalities enable users to efficiently understand validation results, identify problematic areas in the validated KG, and take precise corrective actions on their data. Resource Type: Community Shared Software Framework. License: AGPL-3.0 license. Demo: https://purl.org/shacl-dashboard . Source Code: https://github.com/DE-TUM/shacl-dashboard . Datasets: https://zenodo.org/records/15400008 .

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SHACL Dashboard: Analyzing Data Quality Reports Over Large-Scale Knowledge Graphs

  • 会议论文

  • 发表日期：

    2026年

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  • Johannes Mäkelburg,
  • Zenon Zacouris,
  • Jin Ke,
  • Maribel Acosta

摘要

Validating knowledge graphs (KGs) ensures their quality and reliability in real-world applications. The Shapes Constraint Language (SHACL) has emerged as a recommended language for validating RDF KGs, by defining structured constraints. Many organizations leverage SHACL validation and its reports to detect problems, guide corrections, and improve data quality. Yet, large-scale KGs often produce extensive validation reports, making manual analysis infeasible. To address this challenge, we present the SHACL Dashboard, a novel online tool for visualization and multidimensional analysis of SHACL validation reports. SHACL Dashboard provides analytical plots, and fine-grained insights into individual constraints. These functionalities enable users to efficiently understand validation results, identify problematic areas in the validated KG, and take precise corrective actions on their data. Resource Type: Community Shared Software Framework. License: AGPL-3.0 license. Demo: https://purl.org/shacl-dashboard . Source Code: https://github.com/DE-TUM/shacl-dashboard . Datasets: https://zenodo.org/records/15400008 .