知识图谱技术及其在农业领域应用研究进展

随着农业大数据时代的到来,如何开展直观的有效信息挖掘成为数据利用的一大难题。作为一种能够帮助人们高效地管理现实世界中事物及其关系的异构语义网络,知识图谱应用在近年来备受关注。在农业数据不断增加、结构越来越复杂的背景下,将知识图谱应用于农业领域有助于农业大数据分析,促进智慧农业发展。该文首先分析了知识图谱构建的模式,即自顶向下、自底向上及两种模式结合等3种模式的特点,然后从本体构建、知识抽取、知识融合、知识推理、知识图谱存储及可视化5个方面综述了农业知识图谱构建的关键技术应用进展与难点,接着对当前知识图谱在农业领域的应用进行了梳理,主要有农业专题文献计量研究、农业信息检索、农业知识问答和农业信息资源推荐等4个方面,最后对知识图谱技术在农业领域的应用研究方向进行了展望,认为未来应关注基于知识图谱的农产品电商推荐、动态农业知识图谱的构建、跨领域知识图谱的构建与关联等方面。

Research progress on knowledge graph technology and its application in agriculture

An accurate and rapid analysis of massive data can be one of the most important steps for the comprehensive utilization of agricultural big data, particularly with the advent of the era of big data. Among them, the knowledge graph can be expected to represent complex domain knowledge using data mining, information processing, knowledge statistics, and graph drawing. The dynamic development of knowledge can also be revealed to provide a practical and valuable reference for complicated research. Therefore, the knowledge graph has attracted much attention in recent years, due mainly to the heterogeneous semantic network. Efficient management can be achieved in the things and their relationships in the real world. The efficient capability of information retrieval can be attributed to the storage structure of the knowledge graph, namely the directed graph. The knowledge graph can be applied to intuitively display the complex data under the background of the increasing data volume and complex structure in the agricultural field. Agricultural big data can be systematically analyzed for the high utilization of data value, and the mining of agricultural data rules, in order to promote the development of smart agriculture. The key technology of knowledge graph construction can dominate the knowledge graph research in the agricultural field. The agricultural knowledge graph should follow the general technology specification of knowledge graph construction. This review aims to explore the theoretical support of knowledge graphs in agricultural application. Firstly, the construction models of the knowledge graph were divided into three types: top-down, bottom-up, top-down and bottom-up combination. Among them, the top-down and bottom-up combination of construction model was the most commonly used with the more completed and flexible structure suitable for the knowledge graph construction in specific fields. Secondly, the key technologies of agricultural knowledge graph construction were summarized from five aspects: ontology construction, knowledge extraction, knowledge fusion, knowledge reasoning, knowledge graph storage and visualization. The progress of each aspect was then compared, including the technical difficulties, technological evolution, innovation and application exploration. It was found that Protégé tools and semi-automatic construction were widely adopted to construct the knowledge graph in the agricultural field. Furthermore, one of the most concerned research hotspots was knowledge extraction as the premise of construction. The best performance was obtained in the BERT-BiLSTM-CRF among the various knowledge extraction. The difficulty of knowledge graph construction was focused mainly on the terms recognition among agricultural subfields. Particularly, there was the great influence of the natural environment and climate on agricultural knowledge. The application research was also reviewed, including agricultural thematic literature metrology research, agricultural knowledge Q&A, agricultural information resources recommendation, and agricultural information retrieval, as the knowledge graph was gradually applied to the agricultural field. The ontology construction, knowledge extraction, knowledge graph storage, and visualization technologies were commonly used in the above application scenarios, but knowledge fusion and knowledge reasoning were rarely used, indicating the nonstandard knowledge graph construction under specific application backgrounds. Therefore, the agricultural knowledge graph should pay more attention to the cutting-edge construction technologies in the future, in order to innovate in combination with the characteristics of agricultural data. Finally, the future research trends of the knowledge graph can be expected to serve as the e-commerce recommendation for agricultural products. Two research directions were then proposed in the dynamic updating on the construction and correlation of knowledge graphs across the domain.