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基于知识图谱的高速列车知识融合方法

王淑营 李雪 黎荣 张海柱

王淑营, 李雪, 黎荣, 张海柱. 基于知识图谱的高速列车知识融合方法[J]. 西南交通大学学报, 2024, 59(5): 1194-1203. doi: 10.3969/j.issn.0258-2724.20220193
引用本文: 王淑营, 李雪, 黎荣, 张海柱. 基于知识图谱的高速列车知识融合方法[J]. 西南交通大学学报, 2024, 59(5): 1194-1203. doi: 10.3969/j.issn.0258-2724.20220193
WANG Shuying, LI Xue, LI Rong, ZHANG Haizhu. Knowledge Fusion Method of High-Speed Train Based on Knowledge Graph[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1194-1203. doi: 10.3969/j.issn.0258-2724.20220193
Citation: WANG Shuying, LI Xue, LI Rong, ZHANG Haizhu. Knowledge Fusion Method of High-Speed Train Based on Knowledge Graph[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1194-1203. doi: 10.3969/j.issn.0258-2724.20220193

基于知识图谱的高速列车知识融合方法

doi: 10.3969/j.issn.0258-2724.20220193
基金项目: 国家重点研发计划(2020YFB1708000);四川省重大科技专项(2022ZDZX0003)
详细信息
    作者简介:

    王淑营(1974—),女,教授,博士,研究方向为智能制造、工业大数据应用、复杂装备知识图谱,E-mail:w_shuying@126.com

  • 中图分类号: U270;TP391.1

Knowledge Fusion Method of High-Speed Train Based on Knowledge Graph

  • 摘要:

    为解决高速列车各领域知识之间关联不明、难以检索和应用等问题,首先分析高速列车多源异构知识的组织形式,并结合高速列车产品结构树和阶段领域,构建高速列车领域知识图谱模式层和知识图谱;其次,通过双向编码变换器-双向长短期记忆网络-条件随机场(BERT-BILSTM-CRF)模型进行实体识别,得到阶段领域本体的映射;然后,将高速列车实体属性分为结构化和非结构化2类,并分别使用Levenshtein距离和连续词袋模型-双向长短期记忆网络(CBOW-BILSTM)模型计算相应属性的相似度,得到对齐实体对;最后,结合高速列车产品编码结构树进行映射融合,构建高速列车领域融合知识图谱. 应用本文方法对高速列车转向架进行实例验证的结果表明:在命名实体识别方面,基于BERT-BILSTM-CRF模型得到的实体识别准确率为91%;在实体对齐方面,采用Levenshtein 距离、CBOW-BILSTM模型计算实体相似度的准确率和召回率的调和平均数(F1值)分别为82%、83%.

     

  • 图 1  基于知识图谱的高速列车知识融合

    Figure 1.  Knowledge fusion of high-speed train based on knowledge graph

    图 2  高速列车产品结构映射

    Figure 2.  Mapping of high-speed train product structure

    图 3  本体模式构建流程

    Figure 3.  Construction process of ontology pattern

    图 4  领域本体映射流程

    Figure 4.  Mapping process of domain ontology

    图 5  基于属性相似度的实体对齐算法流程

    Figure 5.  Entity alignment algorithm based on attribute similarity

    图 6  语义相似度模型

    Figure 6.  Model of semantic similarity

    图 7  实体映射融合流程

    Figure 7.  Fusion process of entity mapping

    图 8  故障领域本体

    Figure 8.  Ontology of fault domain

    图 9  故障领域知识图谱

    Figure 9.  Knowledge graph of fault domain

    图 10  属性权值测试

    Figure 10.  Attribute weight test

    图 11  相似度阈值取值计算

    Figure 11.  Calculation of similarity threshold value

    图 12  融合本体

    Figure 12.  Fusion ontology

    图 13  设计域和运维域融合知识图谱

    Figure 13.  Fusion knowledge graph of design domain and maintenance domain

    表  1  结构树划分

    Table  1.   Partition of structure trees

    结构树 知识来源 特点分析
     产品族主结构树  产品族模型数据、标准类数据、模板类数据  具有快速重用的特点,不涉及具体的参数值,是设计实例的模板结构,具有元节点编码作为唯一标识
     产品设计结构树  需求数据、几何数据、设计规则、物理属性数据、工艺数据  与设计产出相对应,是按需求设计实例化的结果,具有模块编码作为唯一标识
     产品实例结构树  工艺质量数据、故障数据、制造成本数据  设计实例实物化的结果,与设计实例具有多对一的关系,制造码为唯一标识
    下载: 导出CSV

    表  2  高速列车实体属性

    Table  2.   Entity attributes of high-speed train

    数值型(结构化属性)文本型(非结构化属性)
     运营速度、转向架最大宽度、转向架最大高度、车轮直径(新轮)、车轮直径(半磨耗)、齿轮中心距、轴重 转向架型式、车轮型式、车轮踏面型式、车轴型式、牵引电机型式、牵引拉杆材料、齿轮箱材料
    下载: 导出CSV

    表  3  单位和约束匹配模板

    Table  3.   Matching template of unit and constraint

    约束单位
    不大于mm
    不大于%
    不得超过L
    ±g
    下载: 导出CSV

    表  4  数据集构成

    Table  4.   Composition of dataset

    数据集 实体数 关系数 实体数
    可对齐 不可对齐
    故障数据 13258 41152 8925 4333
    维修数据 10506 35282 8925 1581
    下载: 导出CSV

    表  5  BERT-BILSTM-CRF模型参数

    Table  5.   Parameters of BERT-BILSTM-CRF model

    参数名 参数值
    批大小/批 4
    学习率 0.001
    丢失率 0.5
    训练轮次/轮 10
    字向量维度/维 768
    序列长度/个 128
    下载: 导出CSV

    表  6  实体识别对比实验

    Table  6.   Comparative experiment of entity recognition %

    实验方法 准确率 召回率 F1 值
    Word2vec-BILSTM 86 83 84
    Word2vec-BILSTM-CRF 90 87 88
    BERT-BILSTM 89 86 87
    BERT-BILSTM-CRF 91 88 89
    下载: 导出CSV

    表  7  相似度计算对比实验

    Table  7.   Comparative experiment of similarity calculation

    相似度计算方法 F1 值/%
    Levenshtein 距离 82
    Jaro-Winkler 距离 79
    语义相似度(CBOW) 77
    语义相似度(BILSTM) 81
    语义相似度(CBOW-BILSTM) 83
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-16
  • 修回日期:  2022-07-04
  • 网络出版日期:  2024-05-29
  • 刊出日期:  2022-07-11

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