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Despite rapid progress in machine learning and large language models, most systems remain prediction-centric, weakly auditable, and difficult to align with governance requirements. Meanwhile, the Semantic Web community provides mature foundations for knowledge representation, constraint validation, provenance, and reasoning. However, these strands remain fragmented and rarely integrated into end-to-end reasoning systems. SAGE addresses this gap by bringing together researchers working on knowledge graphs, ontologies, rules, provenance, causal reasoning, and hybrid LLM+KG pipelines. 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