【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%.更多还原

【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.更多还原