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 semantics. Moreover, there are a wide range of database servers available, and a unilingual NLI model limits its practical usage. In this article, we propose to not only address the spatial domain generalization challenge, but also support multilingual back-end, i.e., supporting different query languages, such as SQL and Prolog. For the challenge of spatial semantics, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of context and effectively resolve the ambiguity given the spatial semantics. 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. We also propose to add a prefix symbol to support the multilingual back-end (e.g., query languages). With our spatial comprehension model and symbol injections, 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.

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，由于空间语义的特质和表现力，在一般领域训练的 NLI 很难应用于空间领域。此外，可用的数据库服务器种类繁多，单一语言的 NLI 模型限制了其实际使用。在本文中，我们提出不仅要解决空间域泛化挑战，还要支持多语言后端，即支持不同的查询语言，例如 SQL 和 Prolog。对于空间语义的挑战，我们提出了一种空间理解模型，能够基于上下文的语义识别空间实体的含义，并有效地解决给定空间语义的歧义。然后将从空间理解模型中学习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。我们还建议添加前缀符号以支持多语言后端（例如，查询语言）。通过我们的空间理解模型和符号注入，我们的空间域 NLI，命名为 我们还建议添加前缀符号以支持多语言后端（例如，查询语言）。通过我们的空间理解模型和符号注入，我们的空间域 NLI，命名为 我们还建议添加前缀符号以支持多语言后端（例如，查询语言）。通过我们的空间理解模型和符号注入，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。