Knowledge Fusion Method of High-Speed Train Based on Knowledge Graph
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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BERT-BILSTM-CRF（双向编码变换器-双向长短期记忆网络-条件随机场）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两类，并分别使用Levenshtein距离和CBOW-BILSTM（连续词袋模型-双向长短期记忆网络）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F1值（准确率和召回率的调和平均数）分别为82%、83%.
Abstract:To address challenges of unclear correlation, intricate knowledge retrieval, and difficult knowledge application across diverse domains of high-speed trains, the organizational structure involving multi-source heterogeneous knowledge pertaining to high-speed trains was first analyzed, and a knowledge graph pattern layer and knowledge graph of the high-speed train domain was developed based on the product structure tree and stage domain of high-speed trains. Subsequently, the bidirectional encoder transformer-bidirectional long short-term memory network-conditional random field (BERT-BILSTM-CRF) model was employed for entity recognition, so as to establish the mapping of stage domain ontology. Then, the entity attributes of high-speed trains were categorized into structured and unstructured attributes. The Levenshtein distance and the continuous bag of words-bidirectional long short-term memory network (CBOW-BILSTM) model were utilized to calculate the similarity of corresponding attributes, resulting in aligned entity pairs. Ultimately, the knowledge fusion graph of high-speed train domain fusion was constructed by using the coding structure tree of high-speed train products for mapping and fusion. The proposed method was applied to high-speed train bogies for verification. The results reveal that in terms of named entity recognition, the entity recognition accuracy of the BERT-BILSTM-CRF model reaches 91%. In terms of entity alignment, the F1 values (the harmonic mean of accuracy and recall) of entity similarity calculated by the Levenshtein distance and the CBOW-BILSTM model are 82% and 83%, respectively.
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Key words:
- high-speed train /
- knowledge graph /
- knowledge fusion /
- ontology mapping /
- entity alignment
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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 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 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 
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表 2 高速列车实体属性
Table 2. Entity attribute 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
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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
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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
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表 6 实体识别对比实验
Table 6. Contrast 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
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表 7 相似度计算对比实验
Table 7. Contrast 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
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