摘要
传统大豆数据库存在知识涵盖面狭窄、无效信息繁杂的问题,导致大豆种植者无法通过互联网有效地解决生产难题。知识图谱提供了一种从海量文本和图像数据中抽取知识的手段,使得使用者能够快速有效地检索到所需要的信息。因此,首先根据现有公开资料构建大豆种植管理知识图谱并基于此搭建问答系统,旨在帮助大豆种植者解决种植过程中遇到的问题。具体地,首先采取自顶向下的知识图谱构建方法,采集已有知识和专业领域的先验知识,使用BIO方法标注数据;然后,使用Bert-BiLSTM-CRF模型抽取实体后搭建知识图谱。最后,通过使用Bert-BiLSTM-CRF模型和Bert+TextCNN模型,分别完成问答系统中的命名实体识别任务和用户意图判断任务,再基于上述两个模型进行问答系统的搭建。实验结果表明,构建的大豆种植管理知识问答系统能够有效回答种植过程遇到的问题,证明了问答系统具有一定的实际应用价值。
The traditional soybean database has a narrow range of knowledge coverage and complicated invalid information,which makes it impossible for soybean growers to effectively solve production problems in the Internet.Knowledge graphs provide a way to extract knowledge from massive text and image data,enabling users to quickly and effectively retrieve information.Therefore,this paper firstly constructs a soybean planting management knowledge graph based on existing open information,and builds a related question and answer system to help soybean growers solve problems encountered in the planting process.Specifically,the paper uses a top-down knowledge graph construction method to collect existing knowledge and prior knowledge in professional fields,and uses BIO method to label data.Then,it constructs the knowledge graph after extracting entities through Bert-BiLSTM-CRF model.Finally,by using the Bert-BiLSTM-CRF model and the Bert+TextCNN model,it completes the named entity recognition task and the user intent judgment task to build the question and answer system.Experimental results show that the soybean planting management knowledge question and answer system constructed in this paper can effectively answer problems encountered in the planting process,which proves that the question and answer system can be applied in the practice.
作者
郑鑫鑫
陈凡
孙宝丹
巩建光
江俊慧
ZHENG Xinxin;CHEN Fan;SUN Baodan;GONG Jianguang;JIANG Junhui(College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China;National Key Laboratory of Smart Farm Technologies and Systems,Harbin 150001,China)
出处
《计算机科学》
北大核心
2025年第S1期196-203,共8页
Computer Science
基金
黑龙江省重点研发计划(2022ZX01A23)。
关键词
命名实体识别
意图推断
知识图谱
种植管理
智慧农业
Named entity recognition
Intent inference
Knowledge graph
Planting management
Smart agriculture
