An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment

The paper introduces an explainable, header‑centric framework for semantic table interpretation and data quality assessment that operates solely on column headers. It maps headers to 39 interpretable types using curated lexical resources, applies validation rules to detect data quality issues, and aggregates these into a lightweight quality metric called HeadersIQ. The framework was evaluated on diverse benchmarks, demonstrating broad coverage of noisy real‑world metadata and providing a workflow for metadata‑driven semantic annotation, quality monitoring, and benchmark diagnosis.

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• benchmarks
• safety
• data-engineering