Article(id=1304388152513286405, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388108988997783, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.12.020, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1768406400000, receivedDateStr=2026-01-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788919969919, onlineDateStr=2026-09-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788919969919, onlineIssueDateStr=2026-09-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788919969919, creator=13701087609, updateTime=1788919969919, updator=13701087609, issue=Issue{id=1304388108988997783, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='12', pageStart='4509', pageEnd='4948', issueExtLink='null', onlineDate='null', pubDate='1782576000000', pubDateStr='2026-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1788919959542, creator='13701087609', updateTime=1788923461082, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304402795579330582, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388108988997783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304402795579330583, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304388108988997783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4760, endPage=4779, ext={EN=ArticleExt(id=1304388152798499079, articleId=1304388152513286405, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Visual analysis of research progress and trends in field of traditional Chinese medicine using knowledge graph technology based on bibliometrics and global patents, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To comprehensively sort out and visually analyze the research status, hotspots and trends of knowledge graph technology in the field of traditional Chinese medicine (TCM) from the perspectives of bibliometrics and global patents, provide references for in-depth research and development in this field. Methods The relevant Chinese and English literature published between January 1, 2012, and December 31, 2025, was systematically retrieved from the CNKI, Wanfang, VIP, and Web of Science databases. Using tools such as CiteSpace, VOSviewer, and Excel, we performed bibliometric and visual analyses of publication trends, institutional and author collaboration networks, journal sources, and keyword co-occurrence and evolution. Additionally, we analyzed global patent applications from the Incopat database, focusing on application trends, geographical distribution, applicants, and technical themes. Results A total of 745 valid publications were included (618 in Chinese, 127 in English) and 432 patent families. Research on knowledge graph technology in TCM is in a phase of rapid development. Current hotspots focus on refining knowledge extraction techniques, developing KG-assisted diagnosis and treatment systems, intelligent question-answering, and synergistic applications with large language models. The field is moving toward deeper interdisciplinary integration and clinical application . China occupies a dominant position in both research output and patent applications (96% share), with established collaboration networks. However, cross-regional and interdisciplinary cooperation requires further strengthening. Conclusion Research on knowledge graph technology in the field of traditional Chinese medicine is deeply evolving from fundamental construction to intelligent applications. Despite China’s quantitative advantage in patents, challenges persist, including a relative lack of high-value patents, technical homogenization, and insufficient international competitiveness., authors=TAO Yizhen, WANG Xuejing, ZHENG Weizhe, JIANG Huizhen, XI Yibin, HE Yingrou, LIN Bin, LUO Guangbo, authorsList=TAO Yizhen, WANG Xuejing, ZHENG Weizhe, JIANG Huizhen, XI Yibin, HE Yingrou, LIN Bin, LUO Guangbo, 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=1304388152735584518, articleId=1304388152513286405, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=基于文献计量学和全球专利的知识图谱技术在中医药领域研究进展与趋势可视化分析, columnId=1304140194819629763, journalTitle=中草药, columnName=数据挖掘与循证医学, runingTitle=null, highlight=null, articleAbstract=目的 从文献计量学和全球专利的角度对知识图谱技术在中医药领域的研究现状、热点及趋势进行全面梳理与可视化分析，以期为该领域的深入研究与发展提供参考。方法 系统性检索中国知网（CNKI）、万方（Wanfang）、维普（VIP）、Web of Science（WOS）中2012年1月1日—2025年12月31日的相关中英文文献。利用NoteExpress进行文献管理，并利用CiteSpace、VOSviewer、Excel等分析工具，从发文趋势、机构分布、核心作者合作网络、期刊来源、关键词共现与聚类、时区图等方面进行计量学分析与可视化呈现。通过Incopat专利数据库从专利申请趋势、全球地域分布、专利申请人及专利技术主题等方面检索并分析知识图谱技术在中医药领域相关全球专利申请情况。结果 共纳入745篇有效文献（中文618篇、英文127篇）及432件同族专利。知识图谱技术在中医药领域仍处于快速发展阶段，近期研究热点主要集中于知识抽取技术的改良、知识图谱辅助诊疗、智能问答系统以及大模型与知识图谱的协同研究上，正朝着多学科深度交叉、前沿技术融合与临床场景深耕的方向发展。中国在该领域的发文量占据绝对核心，同时已形成较为稳定的合作网络，但跨地域、跨学科的深度协作有待加强。专利分析表明，全球专利申请量快速增长，中国占据96%的主导地位。结论 知识图谱技术在中医药领域的研究正从基础构建向智能化应用深度演进，虽然我国在专利数量上占优，但仍面临有效专利不足、技术同质化等挑战。未来仍需加强跨学科协同、优化专利布局，以推动该领域的高质量发展与临床转化。, authors=陶奕臻¹, 王雪婧², 郑帏蔗¹, 蒋慧珍¹, 郄谊彬³, 何颖柔³, 林彬⁴, 罗广波¹, authorsList=陶奕臻, 王雪婧, 郑帏蔗, 蒋慧珍, 郄谊彬, 何颖柔, 林彬, 罗广波, authorCompany=1 广州中医药大学第一临床医学院,广东 广州 510405;
2 广州中医药大学 科技创新中心,广东广州 510405;
3 广州中医药大学第二临床医学院,广东 广州 510405;
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