从浅层语义分析到知识智能:大模型驱动下全文本计量研究范式转型OA

[Purpose/significance]In September 2025,the 8th Chengdu Conference on Scientometrics&Evaluation,hosted by the National Science Library(Chengdu),Chinese Academy of Sciences,and undertaken by the Scientometrics&Evaluation Research Center(SERC),was held in Chengdu.This article reviews the Academic Salon on Full-Text Bibliometric Analysis at the forum,summarizing methodological advances,application expansion,and paradigm shifts in full-text bibliometrics in the era of large language models(LLMs),and providing references for future research.[Method/process]Based on invited talks and on-site discussions,this article synthe-sizes the research paradigms and application directions of LLM enabled full-text bibliometrics.It highlights key viewpoints and practical approaches across topics including intelligent data annotation,fine-grained knowledge understanding,knowledge relation inference,knowledge tracing and flow analysis,scientific document understanding and automated review generation,and scientific research evalua-tion.[Result/conclusion]LLMs are driving full-text bibliometrics from shallow semantic analysis centered on topic terms and citation structures toward knowledge intelligence grounded in fine-grained knowledge understanding and knowledge reasoning.Information sci-ence research is undergoing notable changes in data utilization,interpretation of knowledge units,reasoning practices,explanation of bib-liometric outcomes,and human-AI collaboration.Nevertheless,evaluating the reliability of LLM outputs,achieving a prospective and critical comprehension of scientific literature,and verifying the validity of bibliometric metrics are still the main challenges.