NALMO: A Natural Language Interface for Moving Objects Databases

Xieyang Wang, Jianqiu Xu, Hua Lu

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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 four kinds of queries including time interval queries, range queries, nearest neighbor queries and trajectory similarity queries. We develop the system in a prototype system SECONDO and evaluate our approach using 240 natural language queries extracted from popular conference and journal papers in the domain of moving objects. Experimental results show that (i) NALMO achieves accuracy and precision 98.1 and 88.1, respectively, and (ii) the average time cost of translating a query is 1.47s.

Original languageEnglish
Title of host publicationProceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021
Number of pages11
PublisherAssociation for Computing Machinery
Publication date23 Aug 2021
ISBN (Electronic)9781450384254
Publication statusPublished - 23 Aug 2021
Event17th International Symposium on Spatial and Temporal Databases, SSTD 2021 - Virtual, Online, United States
Duration: 23 Aug 202125 Aug 2021


Conference17th International Symposium on Spatial and Temporal Databases, SSTD 2021
Country/TerritoryUnited States
CityVirtual, Online
SeriesACM International Conference Proceeding Series

Bibliographical note

Funding Information:
This work is supported by National Natural Science Foundation of China (61972198), Natural Science Foundation of Jiangsu Province of China (BK20191273).


  • moving objects database
  • natural language interface
  • query processing
  • semantic parsing
  • structured language

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