An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment
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.
知识图谱（KG）的质量不仅取决于下游的图验证，还取决于集成前所使用的表格元数据的质量。在仅限元数据的语义表格解释（STI）中，当单元格值不可用、存在噪声或不适用时，列标题便成为可追溯知识图谱准备工作的关键语义证据来源。
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.
我们提出了一种可解释的、以标题为中心的框架，用于仅限元数据的列类型标注（CTA）和数据质量评估（DQA）。该框架利用精选的词汇资源将标题映射到 39 种可解释的 FinalFormat 类型，并通过 SourceKeywords 保留了标记级的可追溯性。
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.
每种分配的类型都会激活基于数据质量问题（DQI）分类法的验证规则，从而产生诸如缺失数据、重复项、领域违规、错误数据类型和时间不匹配等检测结果。这些检测结果被汇总为 HeadersIQ，这是一种轻量级的、无权重的数据源级质量指标。
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.
该框架在包括 UCI、Prague、Kaggle、VizNet/Sato、SOTAB、T2Dv2 以及 SemTab 2024 Metadata-to-KG 赛道在内的异构基准测试中进行了评估，涵盖了约 120,000 个标题列。结果显示，该框架在嘈杂的现实世界元数据中具有广泛的实际覆盖能力，同时平行的知识图谱映射路径支持与 DBpedia 和此 http URL 的对齐。
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.
在 SemTab 2024 Metadata-to-KG 赛道上，官方的 GT-strict 评估结果表现平平。然而，一项盲测诊断审计表明，许多不匹配反映的是基准测试的粒度、别名和本体选择效应，而非完全不可信的标题中心预测。我们报告此审计是为了提供关于分歧模式的诊断证据，而非作为修订后的基准测试性能。
Overall, the paper presents a reusable workflow for metadata-driven semantic annotation, data source-level quality monitoring, and KG-oriented benchmark diagnosis.
总的来说，本文提出了一种可重用的工作流程，用于元数据驱动的语义标注、数据源级质量监控以及面向知识图谱的基准测试诊断。