title: Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
summary: Text-to-SQL aims to translate natural language queries into SQL statements,
  which is practical as it enables anyone to easily retrieve the desired
  information from databases. Recently, many existing approaches tackle this
  problem with Large Language Models (LLMs), leveraging their strong capability
  in understanding user queries and generating corresponding SQL code. Yet, the
  parametric knowledge in LLMs might be limited to covering all the diverse and
  domain-specific queries that require grounding in various database schemas,
  which makes generated SQLs less accurate oftentimes. To tackle this, we propose
  constructing the knowledge base for text-to-SQL, a foundational source of
  knowledge, from which we retrieve and generate the necessary knowledge for
  given queries. In particular, unlike existing approaches that either manually
  annotate knowledge or generate only a few pieces of knowledge for each query,
  our knowledge base is comprehensive, which is constructed based on a
  combination of all the available questions and their associated database
  schemas along with their relevant knowledge, and can be reused for unseen
  databases from different datasets and domains. We validate our approach on
  multiple text-to-SQL datasets, considering both the overlapping and
  non-overlapping database scenarios, where it outperforms relevant baselines
  substantially.
paper_authors._id: 6837b7fdb7eacc2ac90c6acb
paper_authors.name: Jinheon Baek
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:42:58.830Z
paper_authors._id: 63036b6c5c70c21d0ea79d48
paper_authors.avatarUrl: /avatars/a7eb03f5cbd4eaa09fe807bbed8bc0f7.svg
paper_authors.fullname: Jinheon Baek
paper_authors.type: user
paper_authors.user: jinheon
paper_authors._id: 6837b7fdb7eacc2ac90c6acc
paper_authors.name: Horst Samulowitz
paper_authors._id: 6837b7fdb7eacc2ac90c6acd
paper_authors.name: Oktie Hassanzadeh
paper_authors._id: 6837b7fdb7eacc2ac90c6ace
paper_authors.name: Dharmashankar Subramanian
paper_authors._id: 6837b7fdb7eacc2ac90c6acf
paper_authors.name: Sola Shirai
paper_authors._id: 6837b7fdb7eacc2ac90c6ad0
paper_authors.name: Alfio Gliozzo
paper_authors._id: 6837b7fdb7eacc2ac90c6ad1
paper_authors.name: Debarun Bhattacharjya
paper_title: Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
paper_summary: Text-to-SQL aims to translate natural language queries into SQL statements,
  which is practical as it enables anyone to easily retrieve the desired
  information from databases. Recently, many existing approaches tackle this
  problem with Large Language Models (LLMs), leveraging their strong capability
  in understanding user queries and generating corresponding SQL code. Yet, the
  parametric knowledge in LLMs might be limited to covering all the diverse and
  domain-specific queries that require grounding in various database schemas,
  which makes generated SQLs less accurate oftentimes. To tackle this, we propose
  constructing the knowledge base for text-to-SQL, a foundational source of
  knowledge, from which we retrieve and generate the necessary knowledge for
  given queries. In particular, unlike existing approaches that either manually
  annotate knowledge or generate only a few pieces of knowledge for each query,
  our knowledge base is comprehensive, which is constructed based on a
  combination of all the available questions and their associated database
  schemas along with their relevant knowledge, and can be reused for unseen
  databases from different datasets and domains. We validate our approach on
  multiple text-to-SQL datasets, considering both the overlapping and
  non-overlapping database scenarios, where it outperforms relevant baselines
  substantially.
paper_ai_summary: A comprehensive knowledge base enhances text-to-SQL translation by providing diverse, domain-specific information derived from various database schemas, improving SQL accuracy over existing methods.
paper_ai_keywords: Large Language Models (LLMs) knowledge base natural language queries SQL statements database schemas text-to-SQL parametric knowledge