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Schema-Free Text-to-SQL Learns Enterprise Database Structure from Query Logs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel Text-to-SQL framework designed to operate without requiring complete schema documentation, a common challenge in enterprise database environments. This new system learns database structure by analyzing historical query logs, identifying implicit relationships between tables and columns. By constructing a knowledge graph from query patterns and employing entity-driven GraphRAG retrieval, the framework grounds user questions in domain terminology, significantly outperforming traditional embedding-based retrieval methods. Evaluations on an industrial deployment and the Beaver benchmark demonstrated substantial improvements in executability and answer quality, with expert data engineers showing a strong preference for the new system. AI

IMPACT Enables natural language database interfaces in enterprise environments lacking schema documentation.

RANK_REASON The cluster contains a research paper detailing a new method for Text-to-SQL systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Schema-Free Text-to-SQL Learns Enterprise Database Structure from Query Logs

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The cluster contains a research paper detailing a new method for Text-to-SQL systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · \"Omer Faruk Akg\"ul, Nikhil Kapahi, Prajit Reddy Muppidi ·

    Schema-Free Text-to-SQL: Learning Enterprise Database Structure from Query Logs

    arXiv:2610.33946v1 Announce Type: new Abstract: Text-to-SQL systems achieve remarkable accuracy on academic benchmarks, yet performance drops dramatically in enterprise deployments. This gap stems from a fundamental assumption: existing approaches require complete schema document…

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