仅从值进行元数据重构：在无文档数据仓库中恢复列语义
Metadata Reconstruction from Values Alone: Recovering Column Semantics in Undocumented Warehouses
- Independent Researcher USA
- Independent Researcher
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出Rosetta框架，结合确定性分析器与语言模型，在无文档数据仓库中仅从值恢复列语义，经BIRD、i2b2等数据集验证，提升了元数据恢复的准确率与覆盖率。
AI中文摘要：
Text-to-SQL基准测试提供的模式中，列名已表明列的含义，而生产数据仓库则相反：存在模糊标识符、部分或缺失文档。我们首先解决由此产生的问题：仅从数据本身恢复列和值的含义。Rosetta将语言模型置于验证框架内：确定性分析器提取结构证据（值指纹、26种模式库、校验和判定），模型基于该证据提出语义，且每个事实都带有来源信息和受证据类别约束的置信度。在11个BIRD数据库的680对列（标识符已被破坏）上，针对人工文档，该框架在其确定的42%列上的元数据准确率为0.475，而同一模型直接使用时在94%列上的准确率为0.223。在两者均能处理的283列中，该框架生成的文本与单独模型相比并无更优；增益在于选择：确定性证据决定系统是否输出（覆盖率提升0.257[0.128,0.378]），而非输出质量。确定性层是能力检测器，而非能力放大器。骨干替换限定了该结论：文本发现可复现，但提示请求的弃权（不执行）无法迁移；代码强制提交门（先注册预测，在第三个骨干及保留数据库上测量）使所有骨干的无证据覆盖率为0.000。在盲测的i2b2临床数据仓库中，Rosetta仅从值就解码了134个真实ICD-9代码的95.5%，并对所有44个NDC药物代码弃权（不执行）。该目录支持查询时的校准弃权（不执行）：在完全模式不透明情况下，朴素翻译器的执行准确率从0.92降至0.42，而我们的门在59%的覆盖率上达到86%的准确率。负面结果被明确报告，包括我们自身的权威阶梯并非该标题背后的机制。
英文摘要：
Text-to-SQL benchmarks ship schemas whose column names already say what the columns mean. Production warehouses are the inverse: cryptic identifiers, partial or absent documentation. We address the problem they pose first: recovering what columns and values mean from the data itself. Rosetta places a language model inside a verification harness: a deterministic profiler extracts structural evidence (value fingerprints, a 26-pattern library, checksum verdicts), the model proposes semantics conditioned on that evidence, and every fact carries provenance and a confidence bounded by its evidence class. Against human documentation on 680 paired columns across eleven BIRD databases, identifiers destroyed, the harness delivers metadata that is 0.475 accurate on the 42% of columns it commits to, against 0.223 on 94% for the same model used directly. Restricted to the 283 columns where both arms speak, the harness writes no better prose than the model alone; the gain is selection: deterministic evidence governs whether the system speaks (coverage +0.257 [0.128, 0.378]), not how well. The deterministic layer is a competence detector, not a competence amplifier. A backbone swap bounds the claim: the prose finding reproduces, but prompt-requested abstention does not transfer; a code-enforced commit gate (predictions registered first; measured on a third backbone and held-out databases) makes no-evidence coverage 0.000 on every backbone. On a blind i2b2 clinical warehouse Rosetta decodes 95.5% of 134 real ICD-9 codes from values alone and abstains on all 44 NDC drug codes. The catalog supports calibrated abstention at query time: under full schema opacity a naive translator falls from 0.92 to 0.42 execution accuracy while our gate answers at 86% accuracy over 59% coverage. Negative results are reported plainly, including that our own authority ladder is not the mechanism behind the headline.
