农业知识图谱技术研究现状与展望

在当前农业信息化的发展进程中，多数农业子领域面临着数据资源分散、信息整合难度大、知识利用效率低等问题。作为近年来新兴的一种知识表示技术，知识图谱已在部分农业特定领域展现出了强大的语义推理和数据整合能力，同时帮助一些农业上层应用提高了性能。为系统总结近年来农业知识图谱构建与应用方面的研究成果，本文首先阐述了知识图谱基础和农业知识图谱的构建流程，并从本体建模、信息抽取、知识融合以及知识加工4方面总结了构建农业知识图谱所涉及的关键技术。将当前农业知识图谱的应用分为信息检索、问答系统、推荐系统、专家诊断系统和作物预测5方面，并对这些应用工作进行了梳理。最后，对当前农业知识图谱的研究现状进行了总结，并认为未来农业知识图谱可以从多模态知识推理、强时效性知识更新、多语言知识查询、跨领域数据融合以及子领域知识图谱构建等方面加以研究。

Review of Research Status and Prospects of Agricultural Knowledge Graphs

In the current development process of agricultural informatization, most sub-domains of agriculture face challenges such as dispersed data resources, difficulties in information integration, and low efficiency in knowledge utilization. As an emerging knowledge representation technology in recent years, knowledge graph has demonstrated powerful capabilities in semantic reasoning and data integration in specific agricultural domains. Simultaneously, it has enhanced the performance of some upper-level applications in agriculture. To systematically summarize recent research on the construction and application of knowledge graphs in the agricultural domain, the fundamentals of knowledge graphs and the process of agricultural knowledge graph construction were introduced. Furthermore, it summarized the key technologies involved in constructing an agricultural knowledge graph from four aspects: ontology modeling, information extraction, knowledge fusion, and knowledge processing. Subsequently, an overview of the current applications of agricultural knowledge graphs was provided and discussed in five aspects: information retrieval, question-answering systems, recommendation systems, expert diagnostic systems, and crop prediction. In conclusion, the research status of agricultural knowledge graphs was summarized and it was suggested that future research in agricultural knowledge graphs should explore areas such as multimodal knowledge reasoning, timely knowledge updating, multilingual knowledge queries, cross-domain data fusion, and sub-domain knowledge graph construction.