Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions

2026-03-24

AI Summary

This study addresses the absence of a systematic review on natural language interfaces for spatiotemporal databases, which has hindered the evaluation of methodological strengths, limitations, and future directions. Focusing specifically on spatiotemporal data, this work presents the first comprehensive survey in this domain through a systematic literature review. It establishes a dedicated taxonomy, catalogs representative datasets and evaluation metrics, and conducts a comparative analysis of existing approaches. The investigation uncovers common characteristics, critical limitations, and inconsistent evaluation practices across current methods, identifies core research challenges, and proposes several forward-looking research directions. By doing so, this paper provides a foundational framework and strategic guidance to advance the field of natural language interfaces for spatiotemporal databases.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The task of building a natural language interface to a database, known as NLIDB, has recently gained significant attention from both the database and Natural Language Processing (NLP) communities. With the proliferation of geospatial datasets driven by the rapid emergence of location-aware sensors, geospatial databases play a vital role in supporting geospatial applications. However, querying geospatial and temporal databases differs substantially from querying traditional relational databases due to the presence of geospatial topological operators and temporal operators. To bridge the gap between geospatial query languages and non-expert users, the geospatial research community has increasingly focused on developing NLIDBs for geospatial databases. Yet, existing research remains fragmented across systems, datasets, and methodological choices, making it difficult to clearly understand the landscape of existing methods, their strengths and weaknesses, and opportunities for future research. Existing surveys on NLIDBs focus on general-purpose database systems and do not treat geospatial and temporal databases as primary focus for analysis. To address this gap, this paper presents a comprehensive survey of studies on NLIDBs for geospatial and temporal databases. Specifically, we provide a detailed overview of datasets, evaluation metrics, and the taxonomy of the methods for geospatial and temporal NLIDBs, as well as a comparative analysis of the existing methods. Our survey reveals recurring trends in existing methods, substantial variation in datasets and evaluation practices, and several open challenges that continue to hinder progress in this area. Based on these findings, we identify promising directions for future research to advance natural language interfaces to geospatial and temporal databases.

Problem

• Natural Language Interface
• Spatial Database
• Temporal Database
• NLIDB
• Geospatial Query

Innovation

• Natural Language Interface
• Spatiotemporal Database
• NLIDB
• Taxonomy
• Geospatial Query