Moving objects databases (MODs) have been extensively studied due to their wide variety of applications including traffic management, tourist service and mobile commerce. However, queries in natural languages are still not supported in MODs. Since most users are not familiar with structured query languages, it is essentially important to bridge the gap between natural languages and the underlying MODs system commands. Motivated by this, we design a natural language interface for moving objects, named NALMO. In general, we use semantic parsing in combination with a location knowledge base and domain-specific rules to interpret natural language queries. We design a corpus of moving objects queries for model training, which is later used to determine the query type. Extracted entities from parsing are mapped through deterministic rules to perform query composition. NALMO is able to well translate moving objects queries into structured (executable) languages. We support five kinds of queries including time interval queries, range queries, nearest neighbor queries, trajectory similarity queries and join queries. We develop the system in a prototype system SECONDO and evaluate our approach using 280 natural language queries extracted from popular conference and journal papers in the domain of moving objects. Four volunteers give the system satisfaction and related suggestions through three rounds of independent tests. Experimental results show that (i) NALMO achieves accuracy and precision 96.8% and 81.1%, respectively, (ii) the average time cost of translating a query is 1.49s, and (iii) the average satisfaction is 95.5%.

移动对象数据库 (MOD) 由于其广泛的应用，包括交通管理、旅游服务和移动商务，已被广泛研究。但是，MOD 仍然不支持自然语言查询。由于大多数用户不熟悉结构化查询语言，因此弥合自然语言与底层 MOD 系统命令之间的差距至关重要。受此启发，我们设计了一个用于移动物体的自然语言界面，命名为 NALMO。通常，我们使用语义解析结合位置知识库和特定领域的规则来解释自然语言查询。我们设计了一个用于模型训练的移动对象查询语料库，稍后用于确定查询类型。从解析中提取的实体通过确定性规则映射以执行查询组合。NALMO 能够很好地将移动对象查询转换为结构化（可执行）语言。我们支持五种查询，包括时间间隔查询、范围查询、最近邻查询、轨迹相似性查询和连接查询。我们在原型系统 SECONDO 中开发该系统，并使用从移动物体领域的热门会议和期刊论文中提取的 280 个自然语言查询来评估我们的方法。四名志愿者通过三轮独立测试给出系统满意度和相关建议。实验结果表明（i）我们支持五种查询，包括时间间隔查询、范围查询、最近邻查询、轨迹相似性查询和连接查询。我们在原型系统 SECONDO 中开发该系统，并使用从移动物体领域的热门会议和期刊论文中提取的 280 个自然语言查询来评估我们的方法。四名志愿者通过三轮独立测试给出系统满意度和相关建议。实验结果表明（i）我们支持五种查询，包括时间间隔查询、范围查询、最近邻查询、轨迹相似性查询和连接查询。我们在原型系统 SECONDO 中开发该系统，并使用从移动物体领域的热门会议和期刊论文中提取的 280 个自然语言查询来评估我们的方法。四名志愿者通过三轮独立测试给出系统满意度和相关建议。实验结果表明（i）四名志愿者通过三轮独立测试给出系统满意度和相关建议。实验结果表明（i）四名志愿者通过三轮独立测试给出系统满意度和相关建议。实验结果表明（i）NALMO 的准确率和准确率分别达到 96.8% 和 81.1%， (ii)翻译查询的平均时间成本为 1.49 秒， (iii)平均满意度为 95.5%。