Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
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.
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Jinheon Baek
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2025-05-29T07:42:58.830Z
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Jinheon Baek
user
jinheon
6837b7fdb7eacc2ac90c6acc
Horst Samulowitz
6837b7fdb7eacc2ac90c6acd
Oktie Hassanzadeh
6837b7fdb7eacc2ac90c6ace
Dharmashankar Subramanian
6837b7fdb7eacc2ac90c6acf
Sola Shirai
6837b7fdb7eacc2ac90c6ad0
Alfio Gliozzo
6837b7fdb7eacc2ac90c6ad1
Debarun Bhattacharjya
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
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.
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.
Large Language Models (LLMs) knowledge base natural language queries SQL statements database schemas text-to-SQL parametric knowledge