A new method for measuring the originality of academic articles based on knowledge units in semantic networks |
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| Affiliation: | 1. School of Government, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian, Beijing, 100875, China;2. Department of Mechanical Engineering, National Taiwan University, Roosevelt Road, No. 1, Sec. 4, Taipei, 10617, Taiwan;3. Department of Library and Information Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan;1. Hasselt University, Belgium;2. KU Leuven, Belgium, University of Antwerp, Belgium;1. Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China;2. School of Information Management, Wuhan University, Wuhan 430072, China;1. Department of Library and Information Science, National Taiwan University, Taipei, Taiwan;2. Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan;1. Institute of Scientific and Technical Information of China, Beijing 100038, P.R. China;2. College of Economics and Management, Beijing University of Technology, Beijing 100124, P.R. China;3. School of Information Resource Management, Renmin University of China, Beijing 100872, P.R. China;4. Business School, Shandong University of Technology, Zibo 255000, P.R. China;1. Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh;2. Department of Computer Science, Banaras Hindu University, Varanasi 221005, India;3. DST-Centre for Policy Research, Indian Institute of Science, Bengaluru 560012, India |
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| Abstract: | Research on the evaluation of the quality of academic papers is attracting more attention from scholars in scientometrics. However, most previous researches have assessed paper quality based on external indicators, such as citations, which failed to account for the content of the research. To that end, this paper proposed a new method for measuring a paper's originality. The method was based on knowledge units in semantic networks, focusing on the relationship and semantic similarity of different knowledge units. Connectivity and path similarity between different content elements were used in particular networks as indicators of originality. This study used papers published between 2014 and 2018 in three categories (i.e. Library & Information Science, Educational Psychology, and Carbon Nanotubes) and divided their content into three parts (i.e. research topics, research methods and research results). It was found that the originality in all categories increase each year. Furthermore, a comparison of our new method with previous models of citation network analysis and knowledge combination analysis showed that our new method is better than those previous methods when used in measuring originality. |
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