[Submitted on 12 May 2026]
Title:An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment
View a PDF of the paper titled An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment, by Marcelo Valentim Silva and 2 other authors
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Abstract:Knowledge Graph (KG) quality depends not only on downstream graph validation, but also on the quality of tabular metadata used before integration. In metadata-only Semantic Table Interpretation (STI), where cell values are unavailable, noisy, or unsuitable, column headers become a critical source of semantic evidence for traceable KG preparation.
We present an explainable, header-centric framework for metadata-only Column Type Annotation (CTA) and Data Quality Assessment (DQA). The framework maps headers to 39 interpretable FinalFormat types using curated lexical resources and preserves token-level traceability through SourceKeywords. Each assigned type activates validation rules based on a taxonomy of Data Quality Issues (DQIs), producing detections such as missing data, duplicates, domain violations, wrong data type, and temporal mismatch. These detections are aggregated into HeadersIQ, a lightweight, unweighted data source-level quality metric.
The framework was evaluated across heterogeneous benchmarks, including UCI, Prague, Kaggle, VizNet/Sato, SOTAB, T2Dv2, and the SemTab 2024 Metadata-to-KG track, comprising around 120,000 header columns. The results show broad practical coverage across noisy real-world metadata, while a parallel KG-mapping pathway supports alignment to DBpedia and this http URL. On the SemTab 2024 Metadata-to-KG track, the official GT-strict evaluation was modest. However, a blinded diagnostic audit indicates that many mismatches reflect benchmark granularity, aliasing, and ontology-selection effects rather than wholly implausible header-centric predictions. We report this audit as diagnostic evidence on disagreement patterns, not as revised benchmark performance. Overall, the paper presents a reusable workflow for metadata-driven semantic annotation, data source-level quality monitoring, and KG-oriented benchmark diagnosis.
Comments: 18 pages, 4 figures, Workshop on Quality of Knowledge Graphs at ESWC 2026, May 11, 2026, Dubrovnik, Croatia
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2610.10541 [cs.AI]
(or arXiv:2610.10541v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.10541
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Marcelo Valentim Silva [view email] [v1] Tue, 12 May 2026 05:52:55 UTC (218 KB)
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