TYTAN这篇论文教系统自动看懂数据库结构，不用手写语义层，测试里百分之百跑通，挺实在。
TYTAN是一个从关系数据库自动构建分析语义模式的系统，结合符号分析和LLM推理来提出实体、分配角色并命名。在八个数据库的评估中，TYTAN在七个参考域上达到100%的实体与特征覆盖率。其1,678条自生成检索指令全部正确执行，语义角色在92-100%的匹配属性上与参考一致。在一张无声明键的十表实时数据库盲测中，TYTAN恢复了完整实体结构并满足五位独立标注者100%的可满足期望。
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis. Today, this semantic layer is usually written by hand. This is a knowledge-acquisition bottleneck that limits the scalability of analytic systems, keeps non-technical users dependent on experts, and is itself error-prone. We present TYTAN, a system for automatically constructing an analytic semantic schema from a relational database and, when available, a short user-provided description. TYTAN combines symbolic analysis of the database with LLM-based semantic inference for entity proposal, role assignment, and naming. When the evidence leaves a decision ambiguous, TYTAN asks the user a targeted natural-language question. We evaluate TYTAN on eight databases spanning real-world and benchmark domains along the three axes that define a schema's functional utility: (i) coverage, are all important entities and features captured?; (ii) retrieval correctness, do the schema's instructions actually reach the data; and (iii) characterization accuracy, are semantic types correct? Across the seven reference domains, TYTAN reaches every entity, attribute, and aggregable feature of the expert-corrected reference schemas (100% coverage). Additionally, 100% of its retrieval instructions execute correctly (1,678 of 1,678 self-generated claims), and semantic roles agree with the reference on 92-100% of matched attributes. Checking the underlying data showed the small disagreement is in the reference, not in TYTAN. On a held-out blind test (a live, ten-table database with no declared keys), TYTAN recovers the full entity structure with verified keys and satisfies 100% of the satisfiable expectations of five independent blind annotators.