Research on Land and Resources Management and Retrieval Using Knowledge Graph

  Objectives  Aiming at the problem of difficult effective management and rapid application between different data products of land and resources, the study uses the graph database to store the public land cover datasets, including GlobaLand30, FROM-GLC10_2017, GLC_FCS30_2020, etc., on the semantic level to establish a knowledge graph of land resources. It provides a new processing framework for the management, rapid application, and data quality assessment of land and resources data.

  Methods  A new application framework for land cover data product management, knowledge extraction, and data acquisition and update based on administrative divisions is proposed. Anomaly data retrieval algorithms based on graphs are used to explore the consistency of different products, and a knowledge-based fast retrieval algorithm for graph nodes of interest (GNOI) in the graph.

  Results  Through the introduction of the knowledge graph, a dynamically updateable nationwide land resource knowledge graph containing 447 817 nodes and 447 816 relationships has been formed, and it is found that the data accuracy of 92 units may have large errors in the 2 875 administrative units covering the whole country.

  Conclusions  The research has greatly improved the utilization rate of multi-source land cover data products, shortened the time of data preprocessing for researchers, and provided new ideas for the knowledge management and application of land resources.