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An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment
Not provided in the abstract
semantic annotationdata qualityknowledge graphmetadata
2610.10541
Builder Relevance
2h ago70%
Abstract
The paper presents a framework for metadata-only Semantic Table Interpretation that emphasizes the importance of column headers for knowledge graph preparation.
Reality Card
Core Claim
The framework effectively maps headers to 39 interpretable types and assesses data quality issues, providing a reusable workflow for semantic annotation and quality monitoring.
Method / Result
Evaluated across 120,000 header columns with broad practical coverage across noisy real-world metadata.
Limitations
The official evaluation results were modest, indicating potential issues with benchmark granularity and ontology-selection effects.
Paper to code
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