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...
| Authors | Donna Hooshmand, Shubham Shahi, Cameron Barrie et al. |
|---|---|
| Primary category | cs.DB |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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