Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
Authors: name: Jinheon Baek, name: Horst Samulowitz, name: Oktie Hassanzadeh, name: Dharmashankar Subramanian, name: Sola Shirai, name: Alfio Gliozzo, name: Debarun Bhattacharjya

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.