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Journal of Agriculture ›› 2023, Vol. 13 ›› Issue (7): 80-86.doi: 10.11923/j.issn.2095-4050.cjas2022-0067

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Knowledge Graph Construction and Visualization in Soil Fertility and Scientific Crop Fertilizer Management

ZHANG Caili1(), WU Saisai2, LI Wei3, WANG Hui4, CHEN Lei1()   

  1. 1 Agricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, Anhui, China
    2 Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    3 Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, Anhui, China
    4 Soil and Fertilizer Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, Anhui, China
  • Received:2022-06-08 Revised:2022-12-01 Online:2023-07-20 Published:2023-07-18

Abstract:

Aiming to improve the function of artificial intelligence in agriculture, and to search the knowledge organization and application in field of soil fertility, the soil fertility ontology of Anhui Province was built with manual construction method as well as the Protégé application software. Then the structured data, semi-structured data and unstructured data related to soil fertility in Anhui Province were first cleaned and then processed by inverse distance weighted (IDW) interpolation. Based on deep learning model of ERNIE-BiLSTM-CRF and PCNN-Attention, the tasks of named entity recognition and relationship extraction were realized, and all the triplet data were stored in Neo4j graph database. And the visual knowledge graph of soil fertility was successfully constructed. This study can provide reference for the construction of other agricultural knowledge graph in the aspects of ontology construction, entity relationship extraction model and graph visualization.

Key words: soil fertility, ontology construction, entity relationship extraction, deep learning