SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension

TL;DR

SpatialNLI introduces a novel spatial comprehension model that enhances natural language interfaces to spatial databases by accurately capturing spatial semantics and improving query translation accuracy.

Contribution

The paper presents a new spatial comprehension model and information injection technique that significantly improve the accuracy of spatial domain NLIs over existing methods.

Findings

SpatialNLI outperforms state-of-the-art methods in spatial query translation.

The spatial comprehension model effectively captures spatial semantics from context.

Information injection improves the semantic understanding of spatial questions.

Abstract

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods.