NL4ST: A Natural Language Query Tool for Spatio-Temporal Databases
Xieyang Wang, Mengyi Liu, Weijia Yi, Jianqiu Xu, Raymond Chi-Wing Wong

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)…
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Taxonomy
TopicsData Management and Algorithms · Constraint Satisfaction and Optimization · Spatial Cognition and Navigation
