Discovering Interdisciplinarily Spread Knowledge in the Academic Literature

Maiko Kamada,Masaru Isonuma,Kimitaka Asatani,Ichiro Sakata

doi:10.1109/access.2021.3110111

Maiko Kamada, Masaru Isonuma + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3110111

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Abstract

With the increase in scientific publications and the diversification of research areas, science has become complex and interdisciplinary. Discovering important knowledge has become difficult even for researchers in specific domains. Previously proposed keyphrase extraction methods focus mainly on detecting intensively discussed topics in specific domains but do not distinguish concepts with the interdisciplinary spread from those discussed in narrow areas. Here, we propose a diffusion meme score that evaluates the knowledge diffusion distance in a paper citation network. The distance between papers that contain specific terms is measured by the network embedding space of the citation network. Using 57 million publication records from 48 years of Scopus, we evaluated newly appearing terms in and after 1975 in biomedical science papers using the proposed indicator. Approximately half of the top 20 terms were related to Nobel Prize or Clarivate Citation Laureates, and the top terms of the indicators were more likely to appear in Wikipedia than terms extracted using existing methods. Moreover, the top terms were unlikely to include specific minor diseases, which are often extracted using existing methods. Therefore, the diffusion meme score evaluates important terms from scientific literature and citation networks that are more interdisciplinary. Our method improves the understanding of young researchers regarding domains, the development of the history of science, and the evaluation of researcher contributions.

Highlights

As science becomes massive and fragmented into specific domains [1], interdisciplinary research activities play an important role in developing scientific discoveries [2], [3]
We calculated each cluster’s 128-dimensional embedding from the network with DeepWalk. These clusters were visualized in 2D space by using t-distributed stochastic neighbor embedding (T-SNE) for dimension reduction (Fig. 2)
The results indicate that the diffusion meme score can determine terms that are likely to be included in Nobel Prize wins

Summary

Introduction

As science becomes massive and fragmented into specific domains [1], interdisciplinary research activities play an important role in developing scientific discoveries [2], [3]. Recent complex issues have become more important, such as climate change, food security, and healthy aging. In this context, funding agencies [4] and research organizations [5], [6] emphasize the interdisciplinarity of research. In specific domains, such as medical science, new therapies are developed by scientists in many disciplines, such as pharmacologists, molecular biologists, and neuroscientists, and involve scientists of public health, gerontology, zoology, education and engineering [7], [8]. Detecting such interdisciplinarily knowledge is important for gaining a better understanding of scientific achievements

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