基于知识图谱的高速列车知识融合方法

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

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%.

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.