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NALMO: Transforming Queries in Natural Language for Moving Objects Databases

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Abstract

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%.

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Data Availability

The datasets generated and analysed during this study are available at: https://pan.nuaa.edu.cn/share/3208e9a270de8389f9477d1cda or https://pan.baidu.com/s/1TVTPPZmDQ4nT8E7wXOXaAA?pwd=3qjc.

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Acknowledgements

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

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Najing, China

    Xieyang Wang, Mengyi Liu & Jianqiu Xu

  2. Roskilde University, Roskilde, Denmark

    Hua Lu

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  1. Xieyang Wang
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  2. Mengyi Liu
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  3. Jianqiu Xu
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Contributions

Conflicts of reviewers: Ralf Hartmut Güting, rhg@fernuni-hagen.de is the Ph.D advisor of Author Jianqiu Xu. Hua Lu, luhua@ruc.dk, Roskilde University is the co-author of the paper. Zhifeng Bao, zhifeng.bao@rmit.edu.au, RMIT University has the co-authorship with Jianqiu Xu. Ouri E. Wolfson, wolfson@uic.dot.edu, University of Illinois at Chicago has the co-authorship with Jianqiu Xu. Yu Zheng, msyuzheng@outlook.com, JD Finance has the co-authorship with Jianqiu Xu. Bin Yao yaobin@cs.sjtu.edu.cn, Shanghai Jiao Tong University has the co-authorship with Jianqiu Xu.

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Correspondence to Jianqiu Xu.

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Appendix

Appendix

Table 9 Moving objects queries in natural language
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Wang, X., Liu, M., Xu, J. et al. NALMO: Transforming Queries in Natural Language for Moving Objects Databases. Geoinformatica 27, 427–460 (2023). https://doi.org/10.1007/s10707-023-00494-5

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  • DOI: https://doi.org/10.1007/s10707-023-00494-5

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