NLI4DB: A Systematic Review of Natural Language Interfaces for Databases

2025-03-04

AI Summary

This paper addresses three key challenges in natural language interfaces for database querying (NLIDBs): the lack of a unified evaluation framework, the absence of a standardized translation paradigm, and fragmented technical approaches. Methodologically, it proposes a holistic analytical framework comprising three stages—preprocessing, semantic understanding, and query generation—and introduces, for the first time, a three-level translation paradigm applicable to both relational and spatiotemporal databases. It systematically integrates diverse techniques—including dependency parsing, named entity recognition, word embeddings, schema alignment, rule-based engines, supervised/unsupervised learning, and large language models (LLMs)—while unifying LLM-enhanced Text-to-SQL, SQL-to-Text, and speech-to-SQL directions. Contributions include: (1) establishing a standardized evaluation framework; (2) surveying and categorizing mainstream benchmarks and metrics; (3) proposing a principled methodology for constructing novel benchmarks; and (4) identifying two critical research frontiers—deep semantic understanding and dynamic database interaction.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

As the demand for querying databases in all areas of life continues to grow, researchers have devoted significant attention to the natural language interface for databases (NLIDB). This paper presents a comprehensive survey of recently proposed NLIDBs. We begin with a brief introduction to natural language processing techniques, executable database languages and the intermediate representation between natural language and executable language, and then provide an overview of the translation process from natural language to executable database language. The translation process is divided into three stages: (i) natural language preprocessing, (ii) natural language understanding, and (iii) natural language translation. Traditional and data-driven methods are utilized in the preprocessing stage. Traditional approaches rely on predefined rules and grammars, and involve techniques such as regular expressions, dependency parsing and named entity recognition. Data-driven approaches depend on large-scale data and machine learning models, using techniques including word embedding and pattern linking. Natural language understanding methods are classified into three categories: (i) rule-based, (ii) machine learning-based, and (iii) hybrid. We then describe a general construction process for executable languages over relational and spatio-temporal databases. Subsequently, common benchmarks and evaluation metrics for transforming natural language into executable language are presented, and methods for generating new benchmarks are explored. Finally, we summarize the classification, development, and enhancement of NLIDB systems, and discuss deep language understanding and database interaction techniques related to NLIDB, including (i) using LLM for Text2SQL tasks, (ii) generating natural language interpretations from SQL, and (iii) transforming speech queries into SQL.

Problem

• Surveying natural language interfaces for database querying.
• Exploring translation from natural language to executable database language.
• Evaluating benchmarks and techniques for NLIDB system enhancement.

Innovation

• Utilizes traditional and data-driven preprocessing techniques
• Classifies natural language understanding into rule-based, ML-based, hybrid
• Explores LLM for Text2SQL and speech-to-SQL transformations