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sub:assertion {
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    dct:description "Knowledge Graphs (KGs) are increasingly relied upon as semantic infrastructure for AI systems — grounding generative models, supporting retrieval, and serving as machine-actionable memory for autonomous agents. These roles place new demands on KGs that traditional data quality (DQ) dimensions — accuracy, completeness, consistency, timeliness — only partially capture: what matters is no longer just whether facts are correct, but whether a KG’s semantics are sound enough for an AI system to depend on, including well-formed ontological commitments, correctly disambiguated entities and relations, traceable provenance, and machine-interpretable meaning. A KG can score well on conventional DQ metrics and still fail as AI infrastructure, since classical frameworks were designed for static, human-facing data rather than knowledge that machines interpret, traverse, and reason over. This Dagstuhl-style workshop argues that AI usage surfaces a new DQ landscape specific to KGs as semantic artifacts, distinct from DQ for AI in general, and brings together researchers and practitioners from the Semantic Web, Knowledge Graph, data management, and AI communities to identify the DQ dimensions distinctive to KGs in AI contexts and develop a shared research agenda going forward.";
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    schema:location "Pegaso";
    schema:startDate "2026-10-26T09:00:00+01:00"^^xsd:dateTime;
    schema:url <https://redq-workshop.github.io/>;
    rdfs:label "ReDQ: Rethinking Data Quality for Generative AI and Knowledge Graphs";
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sub:pubinfo {
  <https://orcid.org/0000-0002-1267-0234> foaf:name "Tobias Kuhn" .
  
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    rdfs:label "ReDQ: Rethinking Data Quality for Generative AI and Knowledge Graphs";
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