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A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph

Author

Listed:
  • Junnan Liu

    (School of Data and Target Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

  • Haiyan Liu

    (School of Data and Target Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

  • Xiaohui Chen

    (School of Data and Target Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

  • Xuan Guo

    (Institute of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

  • Qingbo Zhao

    (School of Data and Target Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

  • Jia Li

    (School of Data and Target Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

  • Lei Kang

    (School of Data and Target Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

  • Jianxiang Liu

    (School of Data and Target Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

Abstract

Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in handling many semantic intricacies and retrieving geospatial data. In light of this, a heterogeneous retrieval method based on knowledge graph is proposed in this paper. There are three advantages of this method: (1) the semantic knowledge of geospatial data is considered; (2) more information required by users could be obtained; (3) data retrieval speed can be improved. Firstly, implicit semantic knowledge is studied and applied to construct a knowledge graph, integrating semantics in multi-source heterogeneous geospatial data. Then, the query expansion rules and the mappings between knowledge and database are designed to construct retrieval statements and obtain related spatial entities. Finally, the effectiveness and efficiency are verified through comparative analysis and practices. The experiment indicates that the method could automatically construct database retrieval statements and retrieve more relevant data. Additionally, users could reduce the dependence on data storage mode and database Structured Query Language syntax. This paper would facilitate the sharing and outreach of geospatial knowledge for various spatial studies.

Suggested Citation

  • Junnan Liu & Haiyan Liu & Xiaohui Chen & Xuan Guo & Qingbo Zhao & Jia Li & Lei Kang & Jianxiang Liu, 2021. "A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2005-:d:498534
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    References listed on IDEAS

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    1. Stephan Huber & Christoph Rust, 2016. "Calculate travel time and distance with OpenStreetMap data using the Open Source Routing Machine (OSRM)," Stata Journal, StataCorp LP, vol. 16(2), pages 416-423, June.
    2. Ying Zhang & Puhai Yang & Chaopeng Li & Gengrui Zhang & Cheng Wang & Hui He & Xiang Hu & Zhitao Guan, 2018. "A Multi-Feature Based Automatic Approach to Geospatial Record Linking," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(4), pages 73-91, October.
    3. Saad Aloteibi & Mark Sanderson, 2014. "Analyzing geographic query reformulation: An exploratory study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(1), pages 13-24, January.
    4. Tianxing Wu & Guilin Qi & Cheng Li & Meng Wang, 2018. "A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications," Sustainability, MDPI, vol. 10(9), pages 1-26, September.
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    Cited by:

    1. Xuan Guo & Haizhong Qian & Fang Wu & Junnan Liu, 2021. "A Method for Constructing Geographical Knowledge Graph from Multisource Data," Sustainability, MDPI, vol. 13(19), pages 1-17, September.

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