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

作　　者：王淑营[1] 李雪[1] 黎荣[2] 张海柱[2] WANG Shuying;LI Xue;LI Rong;ZHANG Haizhu(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)

机构地区：[1]西南交通大学计算机与人工智能学院,四川成都611756 [2]西南交通大学机械工程学院,四川成都610031

出　　处：《西南交通大学学报》2024年第5期1194-1203,共10页Journal of Southwest Jiaotong University

基　　金：国家重点研发计划(2020YFB1708000);四川省重大科技专项(2022ZDZX0003)。

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%.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.

关 键 词：高速列车 知识图谱 知识融合 本体映射 实体对齐 

分 类 号：U270[机械工程—车辆工程] TP391.1[交通运输工程—载运工具运用工程]