Article(id=1308454723569349023, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.04.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752076800000, receivedDateStr=2025-07-10, revisedDate=1772035200000, revisedDateStr=2026-02-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889516012, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889516012, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889516012, creator=13701087609, updateTime=1789889516012, updator=13701087609, issue=Issue{id=1308454712513156008, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='4', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1789889513376, creator='13701087609', updateTime=1789889839088, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308456079029986178, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308456079029986179, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=107, endPage=113, ext={EN=ArticleExt(id=1308454723825201568, articleId=1308454723569349023, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Construction Method of Rail Transit Operation and Maintenance Knowledge Graphs Based on Large Language Models, columnId=1308453883261514080, journalTitle=Urban Rapid Rail Transit, columnName=Artifical Intelligence, runingTitle=null, highlight=null, articleAbstract=

To address the problems of high manual dependence, complex data processing, and low construction efficiency in constructing knowledge graphs for rail transit operation and maintenance, this paper proposes an automated knowledge graph construction method based on large language models. First, two-stage domain-specific prompt templates for rail transit operation and maintenance are designed for domain concept layer generation and operation and maintenance data extraction, respectively. Second, the zero-shot learning capability of large language models is employed to automatically generate knowledge graph data that conform to RDF syntax specifications. Finally, a prompt-driven knowledge graph construction framework is developed to automatically transform rail transit operation and maintenance metadata into RDF knowledge graphs and support integration with existing knowledge graphs. The experimental results show that, compared with the general prompt, the proposed method improves entity extraction accuracy from 81.2% to 93.8%, an increase of 12.6 percentage points; the completeness of the concept layer increases from 68.4% to 91.6%; and the accuracy of complex relation extraction increases from 46.7% to 78.9%. In the comparison with three triple extraction methods, namely OpenNRE, the Language Technology Platform (LTP), and Bert4Keras,

, authors=Yulong Cui, Junyan Wang, Jinghao Chen, Kexin Sun, authorsList=Yulong Cui, Junyan Wang, Jinghao Chen, Kexin Sun, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308454724110414241, articleId=1308454723569349023, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于大模型的轨道交通运维知识图谱构建方法, columnId=1308453883420897634, journalTitle=都市快轨交通, columnName=人工智能, runingTitle=null, highlight=null, articleAbstract=

针对轨道交通运维知识图谱构建过程中人工依赖程度高、数据处理复杂、构建效率低等问题，提出一种基于大语言模型的自动化知识图谱构建方法。首先，设计面向轨道交通运维领域的双阶段垂域专属提示词模板，分别用于领域概念层生成和运维数据抽取；其次，利用大语言模型的零样本学习能力，自动生成符合资源描述框架（resource description framework, RDF）语法规范的知识图谱数据；最后，构建基于提示词驱动的知识图谱构建框架，实现轨道交通运维元数据向RDF知识图谱的自动转化，并支持与已有知识图谱的融合。实验结果表明：与通用Prompt相比，本文方法的实体抽取准确率由81.2%提升至93.8%；概念层完整性由68.4%提升至91.6%，复杂关系抽取准确率由46.7%提升至78.9%；与OpenNRE、语言技术平台（language technology platform, LTP）、Bert4Keras 3种三元组抽取方法相比，本文方法Prompt+LLM的精确率为94%，召回率为93%，F1值为0.93，抽取时间为562 ms，整体效果最优。该方法有效提高轨道交通运维知识图谱的构建效率和质量，可为轨道交通智能运维决策及其他领域知识图谱自动构建提供参考。

, authors=崔玉龙, 王俊彦, 陈景浩, 孙可心, authorsList=崔玉龙, 王俊彦, 陈景浩, 孙可心, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=Tn4zpDbB0Ul0ogvhDAnr7Q==, magXml=FKQer03iswqLfceNOTdg1Q==, pdfUrl=null, pdf=HNWjR91jt4hyXiv/ftkzzQ==, pdfFileSize=2168950, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=45aMWQjfZYfarIAk5/QKiQ==, mapNumber=null, fund=null)}, authors=[Author(id=1308454725997851046, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1308454723569349023, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=cuiyulong@cqsf.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1308454726085931432, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1308454723569349023, authorId=1308454725997851046, language=EN, stringName=Yulong Cui, firstName=Yulong, middleName=null, lastName=Cui, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=CRRC Qingdao Sifang Locomotive & Rolling Stock Co., Ltd., Qingdao 266111, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1308454726148845993, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1308454723569349023, authorId=1308454725997851046, language=CN, stringName=崔玉龙, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中车青岛四方机车车辆股份有限公司，山东 青岛 266111, bio={"content":"

崔玉龙，男，硕士，正高级工程师，主要从事动车组网络控制及人工智能等方面研究，cuiyulong@cqsf.com

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崔玉龙，男，硕士，正高级工程师，主要从事动车组网络控制及人工智能等方面研究，cuiyulong@cqsf.com

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