学科交叉主题识别方法研究综述

Review of Methods for Interdisciplinary Topic Identification

【目的】通过文献调研梳理总结学科交叉主题识别的各种方法,总结不足与改进方向。【文献范围】以CNKI和Web of Science核心数据库为数据源,针对学科交叉主题识别的相关概念与方法构造检索式,最终确定74篇文献进行综述。【方法】在厘清“学科交叉”内涵及相近概念的基础上,从基于外部特征的识别、基于内部特征的识别及二者结合的识别三种角度出发,对学科交叉主题识别方法进行梳理评述。【结果】现有方法还存在一些不足,如数据源和识别语料单一、识别方法语义性不足、识别粒度较粗、缺少主题级学科交叉测度指标、识别结果缺少前瞻性与动态探索性。【局限】主要选取代表性文献进行综述;未深入阐述交叉主题识别的技术细节;未重点综述学科交叉文献发现的研究;对学科交叉趋势跟踪、学科分类聚类等研究在学科交叉主题识别中的应用覆盖不够。【结论】未来研究应扩展基于多源数据或全文本识别方法,提高识别方法的语义挖掘能力,进行细粒度交叉主题识别,构建多维学科交叉主题测度指标,加强对潜在交叉主题和学科交叉动态趋势的研究。

[Objective]This paper summarizes various methods for interdisciplinary topic identification through a literature review and tries to find shortcomings with potential improvements.[Coverage]We retrieved 74 articles on the concepts and methods of interdisciplinary topic identification from the CNKI and Web of Science databases.[Methods]Based on clarifying the concepts of“interdisciplinarity”and related terms,this paper reviewed the method for interdisciplinary topic identification from three perspectives:recognition based on external characteristics,recognition based on internal features,and a combination of both.[Results]There are still some deficiencies in the existing methods,such as limited data source and identification corpus,insufficient semantics of identification method,coarse identification granularity,a lack of interdisciplinary measurement indicators at the subject level,as well as a lack of forward-looking and dynamic exploration in the identification results.[Limitations]We mainly selected representative literature and did not provide an in-depth exploration of the technical details of interdisciplinary topic identification.We did not review the study of interdisciplinary literature discovery.More research is needed to expand the application of trend tracking and subject clustering in interdisciplinary topic identification.[Conclusions]Future research should expand the identification methods based on multi-source data or full text,improve the semantic mining ability,conduct fine-grained identification,build multi-dimensional interdisciplinary topic measurement indices,and strengthen research on potential interdisciplinary topics and dynamic trends.