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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. 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