Frontiers of Data and Domputing ›› 2021, Vol. 3 ›› Issue (6): 50-59.doi: 10.11871/jfdc.issn.2096-742X.2021.06.004

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Discovering Interdisciplinary Research Based on Word Embedding

HE Tao1,*(),WANG Guifang2(),MA Tingcan2()   

  1. 1. Department of Information Security, Naval University of Engineering, Wuhan, Hubei 430033, China
    2. Wuhan Library, Chinese Academy of Sciences, Wuhan, Hubei 430071, China
  • Received:2021-10-26 Online:2021-12-20 Published:2022-01-26
  • Contact: HE Tao E-mail:taohe@whu.edu.cn;conawang0206@gmail.com;matc@whlib.ac.cn

Abstract:

[Objective] Interdisciplinary research has promoted the emergence of many major scientific discoveries, hence researchers need to identify interdisciplinary problems in their research field. Since huge number of scientific papers have been published today, it is difficult to discover interdisciplinary research manually, automatic searching methods are needed.[Methods] This paper proposes an approach to discover interdisciplinary research automatically. This method adopts the word embedding mechanism in artificial intelligence, which covers approximately 1.7 million vocabularies of natural sciences. It captures interdisciplinary research by automatically identifying the keywords with semantic anomalies from the author's keywords. [Results] The method is applied to the research field of deep learning and discovers some interdisciplinary natural science research. [L-imitations] Due to the shortcomings of traditional word embedding in representing the word with multiple meanings, the recognition accuracy of keywords with semantic anomalies needs to be improved. [Conclusions] The proposed method is a novel solution to the discovery of interdisciplinary research.

Key words: interdisciplinary research, word embedding, semantic anomaly, deep learning