Overview
Knowledge Graph (KG) quality depends not only on downstream graph validation but also on the quality of tabular metadata used prior to 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.
Framework
This work presents 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
- Temporal mismatch
These detections are aggregated into HeadersIQ, a lightweight, unweighted data source-level quality metric.
Evaluation
The framework was evaluated across heterogeneous benchmarks, including UCI, Prague, Kaggle, VizNet/Sato, SOTAB, T2Dv2, and the SemTab 2024 Metadata-to-KG track, comprising approximately 120,000 header columns. Results demonstrate broad practical coverage across noisy real-world metadata, while a parallel KG-mapping pathway supports alignment to DBpedia and Schema.org.
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. This audit is reported as diagnostic evidence on disagreement patterns, not as revised benchmark performance.
Significance
Overall, the paper presents a reusable workflow for metadata-driven semantic annotation, data source-level quality monitoring, and KG-oriented benchmark diagnosis.
Publication Details
- Authors: Marcelo Valentim Silva, Hannes Herrmann, Valerie Maxville
- Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
- Comments: 18 pages, 4 figures, Workshop on Quality of Knowledge Graphs at ESWC 2026, May 11, 2026, Dubrovnik, Croatia
- Cite as: arXiv:2610.10541 [cs.AI]
- Submitted: 12 May 2026
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