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.

提出了一种称为SpatialNLI的NLI用于空间领域，它采用一种空间理解模型来识别空间请求的意义并将其注入到自然语言问题中以缓解捕捉空间专用语义的负担，同时实验结果表明SpatialNLI胜过现有的方法

SpatialNLI: 基于空间理解的空间领域自然语言接口数据库