计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240500025-8.doi: 10.11896/jsjkx.240500025

• 人工智能 • 上一篇    下一篇

基于知识图谱的大豆种植管理知识问答系统

郑鑫鑫1, 陈凡1, 孙宝丹1,2, 巩建光1,2, 江俊慧1,2   

  1. 1 哈尔滨工程大学计算机科学与技术学院 哈尔滨 150001
    2 智慧农场技术与系统全国重点实验室 哈尔滨 150001
  • 出版日期:2025-06-16 发布日期:2025-06-12
  • 通讯作者: 孙宝丹(sunbaodan@hrbeu.edu.cn)
  • 作者简介:(zhengxinxin206828@163.com)
  • 基金资助:
    :黑龙江省重点研发计划(2022ZX01A23)

Question Answering System for Soybean Planting Management Based on Knowledge Graph

ZHENG Xinxin1, CHEN Fan1, SUN Baodan1,2, GONG Jianguang1,2, JIANG Junhui1,2   

  1. 1 College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
    2 National Key Laboratory of Smart Farm Technologies and Systems,Harbin 150001,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:ZHENG Xinxin,born in 2001,postgra-duate.Her main research interest is smart griculture.
    SUN Baodan,born in 1993,Ph.D,lecturer,master supervisor.Her main research interests include machine lear-ning and so on.
  • Supported by:
    Key R&D Program of Heilongjiang Province(2022ZX01A23).

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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。

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

Abstract: 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.

Key words: Named entity recognition, Intent inference, Knowledge graph, Planting management, Smart agriculture

中图分类号: 

  • TP182
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