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

The study presents an explainable method that focuses on table headers to identify column types and assess data quality when only metadata is available. Using curated lexical resources, the system assigns each header one of thirty‑nine structured types, maintaining token‑level traceability through source keywords. Each detected type triggers a set of validation rules organized into a taxonomy of quality problems, enabling the detection of missing information, duplicate values, domain violations, incorrect data types, and temporal mismatches. The findings are consolidated into a lightweight metric called HeadersIQ, which provides an unweighted assessment of the data source’s quality.

The approach was evaluated on a range of benchmark sets that include academic sources, competitions, and open data repositories, covering roughly 120,000 header columns. Results show broad practical coverage against noisy, real‑world metadata, and the process includes a parallel pathway to align results with ontologies such as DBpedia and Schema.org. In the official SemTab 2024 challenge, performance was modest; however, a blind analysis revealed that many discrepancies stem from benchmark granularity, aliases, and ontological choices rather than model failures.

This research is significant because it enhances the ability to generate reliable knowledge graphs from incomplete tables, facilitating data integration and early detection of quality issues that could impact downstream applications.

Read the original article on arXiv cs.AI

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