摘要：

随着农业信息技术的发展, 在互联网中积累了大量与水稻种植相关的数据。为解决农民在种植过程中难以快速获取准确答案的问题, 从水稻种植领域出发, 构建了基于知识图谱的智能问答系统。通过手工收集与爬虫技术获取相关数据, 通过构建命名实体识别模型和意图识别模型等自然语言处理技术并结合前后端技术, 最终实现了水稻种植领域智能问答系统的开发。实验结果表明, 在命名实体识别与意图识别模块中, 所构建模型的F1值分别达到89.17%和96.54%, 均高于其他常见模型。基于知识图谱的水稻种植智能问答系统能够准确回答农民在种植水稻过程中遇到的大部分问题, 实现了对水稻种植知识图谱数据的管理和可视化展示。

关键词: 知识图谱, 命名实体识别, 对抗训练, 意图识别, 卷积神经网络

Abstract:

With the development of agricultural information technology, a substantial amount of rice planting-related data has been accumulated on the Internet. To address the challenges that farmers face in quickly obtaining accurate information during the planting process, an intelligent question-answering system is constructed based on a knowledge graph, specifically for rice planting. First, relevant data are obtained through manual collection as well as web crawler technology. Natural language processing techniques, such as the named entity recognition model and an intent recognition model, are built in conjunction with front- and back-end technologies to develop an intelligent question-answering system for rice planting. Experimental results show that in the named entity recognition and intent recognition modules, the F1 values of the constructed models reach 89.17% and 96.54%, respectively, which are higher than those of other conventional models. The intelligent rice planting question-answering system, based on knowledge graph, can accurately answer most inquiries farmers encounter during the process of rice planting, facilitating the management and visualization of rice planting knowledge graph data.

Key words: knowledge graph, named entity recognition, adversarial training, intent recognition, Convolutional Neural Network(CNN)