学科主题探测与演化分析:多视角述评与展望

Recent Advancements in Detection and Evolutionary Tracking of Scientific Topics:A Multi-perspective Survey and Prospect

如何借助于有效的方法和途径在大量文献数据基础上对学科主题进行深入和精准的探测和跟踪,是以情报学科学计量领域为核心、不同学科共同致力突破的热点和前沿研究问题。相关方法和途径主要涉及频次视角、内容视角、引证视角以及融合视角等四个视角。本研究试图结合近年来发表于国际国内重要期刊有关学科主题探测和演化分析的最新文献,对相关视角的主要进展进行述评,归纳总结不同视角的实现路径和机制,指出已有视角可能存在的知识单元或者网络关系的异质重要性偏差问题、知识的时间衰变以及新兴主题特征的小样本弱势问题、主题自然发育和进化的拟合困境、微观层面的知识流动和变迁刻画问题等,特别地,为融合视角这一总体趋势指明方向。

Leveraging instructive approaches to comprehensively and accurately detect and track topics from the big data of historical literature is a hot and cutting-edge issue that has garnered substantial interest from different disciplines,especially scientometrics in information science.The major underlying mechanisms involve four perspectives,which include frequency,content,citation,and synthesized analyses.This study attempts to review the latest literature published in prestigious international and domestic journals,to update the main progress of relevant perspectives in scientific topic detection and evolution analysis by unravelling their essential implementation paths.In particular,it considers the risks inherited in existing perspectives,including the biases in heterogeneous importance of knowledge units or network relationships,temporal decay of knowledge,trap of the small samples of emergent topics,dilemma in fitting the natural development and evolution of topics,and failure in portraying knowledge flow and evolution in micro level.In response to these issues,the study focuses attention to the future trend of a synthesized perspective.