SpatialNLI：一个基于空间理解的空间域数据库自然语言接口
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
摘要：
数据库的自然语言接口（NLI）是将自然语言问题转换为可由数据库管理系统（DBMS）执行的结构化查询的接口。然而，由于空间问题的特质和表现力，在一般领域中训练的NLI很难应用于空间领域。受机器理解模型的启发，我们提出了一个空间理解模型，它能够根据上下文的语义来识别空间实体的含义。然后将从空间理解模型中学习到的空间语义注入到自然语言问题中，以减轻捕获空间特定语义的负担。通过我们的空间理解模型和信息注入，我们的空间域NLI，命名为SpatialNLI，能够捕获问题的语义结构，并将其准确地转换为可执行查询的相应语法。我们还通过实验确定SpatialNLI优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
