基于多源异构数据和知识图谱的作物病害关联分析

Association Analysis of Crop Diseases Based on Multi-source Heterogeneous Data and Knowledge Graph

摘要:

多源异构农业信息的整合不仅有助于理解和揭示病害侵染与防控路径，也可为病害处方推荐研究提供重要的数据支持。针对电子病历等多源异构农业数据解析中的融合、对齐和异质性问题，本文构建作物病害处方知识图谱，可视化解析病害关联关系。基于病三角原理，解析病原体、宿主及环境在病害侵染与防治过程中的关键作用，结合数据特点构建包含18类本体概念、17条关系和6条属性边的作物病害处方知识图谱本体层；结合基于规则匹配与基于深度学习的知识抽取方法构建知识图谱实体层，共包含1121个实体实例和8292条关系实例；分别开展基于度中心性和介数中心性的Top20关键节点识别和基于Adamic-Adar的病害-防控产品Top5关联预测，可视化解析病害、症状、防控产品等关键实体与属性的关联关系，并从防治方案选择、绿色补贴过滤和相关信息查询3方面制定了优化病害防控产品推荐和信息检索功能的6条规则。研究成果可为基于电子病历数据挖掘的作物病害多目标决策提供支撑。

Abstract:

The integration of multi-source heterogeneous agricultural information not only help understanding and unveiling the pathways of diseases infection and prevention but also offers vital data support for research on diseases prescription recommendations. Aiming at the problems of fusion, alignment and heterogeneity in the analysis of multi-source heterogeneous agricultural data such as electronic medical records, the knowledge graph of crop diseases prescription was constructed, and the diseases association on this basis was visually analyzed. Initially, starting from the principle of the disease triangle, the critical roles of pathogens, hosts, and environment in the process of disease infection and control were analyzed, constructing the ontology layer of the crop diseases prescription knowledge graph with 18 ontology concepts, 17 relationships, and 6 attribute edges based on data characteristics. Subsequently, the entity layer of the knowledge graph was built by integrating rule-matching and deep-learning knowledge extraction methods, encompassing 1121 entity instances and 8292 relationship instances. Lastly, identification of Top20 key nodes based on degree and betweenness centrality, along with Top5 disease-prevention product association predictions using the Adamic-Adar index, were conducted to visually analyze the associations between key entities and attributes, including diseases, symptoms, and prevention products. Six rules were established to enhance the recommendation and information retrieval functions of disease prevention products from three perspectives: prevention scheme selection, green subsidy filtering, and related information inquiry. The research result can provide reference for electronic medical record data mining and association analysis of crop diseases.