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液氧液氢发动机领域知识图谱构建与应用

陈潇萍 王剑锋 张虹 胡庆杰

陈潇萍,王剑锋,张虹,等. 液氧液氢发动机领域知识图谱构建与应用[J]. 北京航空航天大学学报,2024,50(3):821-830 doi: 10.13700/j.bh.1001-5965.2022.0333
引用本文: 陈潇萍,王剑锋,张虹,等. 液氧液氢发动机领域知识图谱构建与应用[J]. 北京航空航天大学学报,2024,50(3):821-830 doi: 10.13700/j.bh.1001-5965.2022.0333
CHEN X P,WANG J F,ZHANG H,et al. Construction and application of knowledge graph in LOX/LH2 engine domain[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):821-830 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0333
Citation: CHEN X P,WANG J F,ZHANG H,et al. Construction and application of knowledge graph in LOX/LH2 engine domain[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):821-830 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0333

液氧液氢发动机领域知识图谱构建与应用

doi: 10.13700/j.bh.1001-5965.2022.0333
详细信息
    通讯作者:

    E-mail:chenxp@calt11.cn

  • 中图分类号: TP391.1;TP18;V434

Construction and application of knowledge graph in LOX/LH2 engine domain

More Information
  • 摘要:

    液氧液氢发动机作为航天领域的关键分系统,为有效开展其研制过程的智能化转型工作,针对液氧液氢发动机领域构建知识图谱,在储备领域知识的同时,提高科研生产人才的培养能力。针对液氧液氢发动机领域特点,对领域语料标注、领域知识识别、实体关系识别3个方面进行了研究,在此基础上进行了领域知识图谱的构建,并从领域知识搜索、知识推荐、探索式分析3个角度梳理了其业务应用模式。形成了液氧液氢发动机领域知识体系,并提出构建方法和应用模式。研究结果为航天领域智能化转型提供了参考。

     

  • 图 1  知识图谱应用

    Figure 1.  Knowledge graph applications

    图 2  液氧液氢发动机领域知识图谱技术架构

    Figure 2.  Liquid oxygen and liquid hydrogen engine domain knowledge graph technical architecture

    图 3  液氧液氢发动机领域知识图谱逻辑架构

    Figure 3.  Logical architecture of knowledge graph for liquid oxygen and liquid hydrogen engine domain

    图 4  领域语料标注结果

    Figure 4.  Domain corpus labelling results

    图 5  关系语料标注结果

    Figure 5.  Relational corpus labelling results

    图 6  3层模型工作流程

    Figure 6.  Three-layer model workflow

    图 7  广义后缀树算法

    Figure 7.  Generalised suffix tree algorithm

    图 8  t时刻下的BiLSTM示例

    Figure 8.  Example of BiLSTM at moment t

    图 9  实体融合过程的决策策略

    Figure 9.  Entity integration strategy

    图 10  知识应用系统的主要功能界面

    Figure 10.  Main functional interface of the knowledge application system

    图 11  知识图谱检索功能界面

    Figure 11.  Knowledge graph retrieval function interface

    图 12  知识实体识别实验结果对比图

    Figure 12.  Comparison of knowledge entity recognition experimental results

    图 13  实体关系识别实验结果

    Figure 13.  Results of the entity relationship recognition experiment

    图 14  AN效果对比

    Figure 14.  AN effect comparison

    表  1  主要扩展属性

    Table  1.   Main extended attributes

    概念类型 属性用途 基本构成
    文档知识 描述获取该文档的方式,及其内容信息 文档标题、来源、等级、业务方向、存储路径、文档内容
    推进剂 描述推进剂的主要成分 成分、配比、储存条件、储存时长、燃烧效率、能量效率
    结构 描述发动机的主要结构技术 点火方式、燃烧室、喷嘴、冷却系统、声学系统、排气系统
    APP 描述APP的基本信息和使用信息 应用名、软件平台、供应商、运营商、简介、内容语言
    下载: 导出CSV

    表  2  实验数据情况

    Table  2.   Experimental data status

    数据集 实体数量 实体对 实体关系
    训练语料 32 384 834 629 60 947
    开发语料 9 102 90 640 5 275
    测试语料 11 286 103 809 7 206
    下载: 导出CSV

    表  3  知识实体识别模型参数设置

    Table  3.   Knowledge entity recognition model parameter settings

    Transformer
    层数
    向量
    维度
    隐藏
    层数
    Dropout 学习率 批大小 迭代
    周期
    12 256 1024 0.5 3×10−5 32 100
    下载: 导出CSV

    表  4  实体关系识别模型参数设置

    Table  4.   Parameterisation of the entity relationship recognition model

    $\theta ({d})$ $\theta ({w})$
    5 {3,4}
    下载: 导出CSV

    表  5  对照参数组

    Table  5.   Control parameter group

    组别 向量维度 隐藏层数 批大小 $ \theta ({\mathrm{dist}})$ $\theta ({\mathrm{wind}})$
    A 128 512 16 4 {3,3}
    B 128 768 16 4 {3,4}
    C 128 1024 16 5 {3,5}
    D 256 512 32 6 {4,5}
    E 256 768 32 6 {4,6}
    下载: 导出CSV

    表  6  知识实体识别实验结果

    Table  6.   Results of knowledge entity recognition experiments

    组别 P R F1
    A 0.852 0.805 0.828
    B 0.894 0.846 0.869
    C 0.902 0.869 0.885
    D 0.879 0.852 0.865
    E 0.910 0.869 0.889
    实验组 0.908 0.882 0.895
    下载: 导出CSV

    表  7  实体关系识别实验结果

    Table  7.   Results of the entity relationship recognition experiment

    组别 P R F1 A5 A10 A20
    A 0.802 0.582 0.675 0.822 0.828 0.830
    B 0.789 0.637 0.705 0.797 0.805 0.813
    C 0.754 0.783 0.768 0.762 0.785 0.800
    D 0.603 0.795 0.686 0.609 0.609 0.615
    E 0.585 0.804 0.677 0.627 0.665 0.709
    实验组 0.772 0.768 0.770 0.798 0.822 0.835
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-07
  • 录用日期:  2022-08-07
  • 网络出版日期:  2022-10-31
  • 整期出版日期:  2024-03-27

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