doi: 10.17706/jsw.20.2.66-83
A New Knowledge Reasoning Scheme for Soil Erosion Based on Knowledge Graph
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Yabo Liu
2,*
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,
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- 1 Key Laboratory of Natural Resource Monitoring and Supervision in the Southern Hilly Region, Ministry of Natural Resources, China
- 2 School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
- Manuscript received July 15, 2025
- revised August 22, 2025
- accepted September 3, 2025
- published October 23, 2025
Abstract
Different types of natural resource data are stored in different databases, which results in them having a low degree of correlation. Combining the data of different natural resources to build a knowledge graph for knowledge reasoning would likely increase the correlation of these data, enabling information mining and assisting in the management of natural resources. We construct a natural resource knowledge graph that interrelates different databases and develop a new knowledge reasoning scheme using remote sensing, vegetation distribution, and soil erosion spatial distribution data from Hunan Province, China. The proposed scheme includes an input layer, unified generation layer, feature extraction layer, knowledge reasoning layer and output layer for analyzing the influence of altitude, soil type and vegetation type on the spatial distribution of soil erosion. The experimental results show that the proposed knowledge reasoning scheme performs well according to the MRR and Hits@N evaluation metrics. Our research provides a scientific basis for predicting the spatial distribution of soil erosion and preventing soil erosion in practical scenarios.
Keywords
- soil erosion prediction
- knowledge graph
How to Cite
Fan Lei, Yabo Liu, Zhichao Zhang, Yifan Hao, and Hao He, "A New Knowledge Reasoning Scheme for Soil Erosion Based on Knowledge Graph," Journal of Software, vol. 20, no. 2, pp. 66-83, 2025. https://doi.org/ 10.17706/jsw.20.2.66-83
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).