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| The Measurement of Scientific Knowledge Structure Based on Journal Clustering and Deep Learning |
| Lu Wanhui1,2,3, Tan Zongying1,2 |
1.National Science Library, Chinese Academy of Sciences, Beijing 100190
2.University of Chinese Academy of Sciences, Beijing 100049
3.Chinese Academy of Social Science Evaluation Studies, Chinese Academy of Social Sciences, Beijing 100732 |
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Abstract Accurately studying and measuring the logical relationship and structural system of scientific knowledge is an important basis for scientific research management activities. As an important platform for the dissemination and exchange of scientific knowledge, academic journals are effective carriers for exploring the structure of scientific knowledge. By using a deep learning algorithm, this paper considers the distance factor in the process of co-citation, studies similarity in journal clustering, and constructs a method for scientific knowledge structure measurement. The experimental results show that the knowledge structure in the humanities and social sciences in China is divided into distinct structures, and journals of different disciplines or research fields are divided into corresponding groups. This indicates that the knowledge structure boundary of the humanities and social sciences in China is relatively clear from the perspective of journal use. This study primarily explored the scientific research topics of the two groups of law journals. From the co-occurrence network of keywords, it can be clearly seen that although the research topics of the two groups of law journals overlap to a certain extent, there are significant differences in the specific research contents between the two groups.
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Received: 17 May 2019
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