Review of Methods for Interdisciplinary Topic Identification
Li Jialei1,2,An Peijun1(),Xiao Xiantao1
1Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China 2Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
[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.
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