NL4ST: A Natural Language Query Tool for Spatio-Temporal Databases

TL;DR

NL4ST is an interactive natural language query tool designed for spatio-temporal databases, enabling non-expert users to formulate complex spatial-temporal queries easily through natural language, supported by a three-layer architecture.

Contribution

The paper introduces NL4ST, a novel natural language query system specifically for spatio-temporal databases, bridging the gap for non-expert users to access complex spatial-temporal data.

Findings

Effective query plans generated for four datasets

System supports natural language queries for complex spatial-temporal data

Demo showcases practical usability and accuracy

Abstract

The advancement of mobile computing devices and positioning technologies has led to an explosive growth of spatio-temporal data managed in databases. Representative queries over such data include range queries, nearest neighbor queries, and join queries. However, formulating those queries usually requires domain-specific expertise and familiarity with executable query languages, which would be a challenging task for non-expert users. It leads to a great demand for well-supported natural language queries (NLQs) in spatio-temporal databases. To bridge the gap between non-experts and query plans in databases, we present NL4ST, an interactive tool that allows users to query spatio-temporal databases in natural language. NL4ST features a three-layer architecture: (i) knowledge base and corpus for knowledge preparation, (ii) natural language understanding for entity linking, and (iii) generating physical plans. Our demonstration will showcase how NL4ST provides effective spatio-temporal physical plans, verified by using four real and synthetic datasets. We make NL4ST online and provide the demo video at https://youtu.be/-J1R7R5WoqQ.