Bibliographic coupling does not always accurately reflect conceptual similarities between research papers, highlighting the need for better ways to understand how documents relate to each other.
Authors
Abhirup Nandy, Department of Computer Science, Banaras Hindu University, Uttar Pradesh, Varanasi, India; CPS Lab, Institute of Informatics and Communication, University of Delhi, Delhi, India
Aakash Singh, Department of Computer Science, Banaras Hindu University, Uttar Pradesh, Varanasi, India; School of Computer Science Engineering and Technology, Bennett University, Uttar Pradesh, Greater Noida, India
Vedika Gupta, Associate Professor, Information Systems and Analytics, Jindal Global Business School, O.P. Jindal Global University, Haryana, Sonipat, 131001, India
Vivek Kumar Singh, Department of Computer Science, Banaras Hindu University, Uttar Pradesh, Varanasi, India; Department of Computer Science, University of Delhi, Delhi, India
Summary
Bibliographic coupling, over the years, has been referred to and used in different contexts related to scientific and technical literature. It is often believed that research papers that have bibliographic coupling deal with similar concepts and hence there may be high conceptual similarity between them. This study attempts to empirically asses this notion. To conduct this research, the study utilizes the data obtained from the Dimensions database and employs advanced machine learning algorithms to extract weighted keywords that better capture the conceptual content of documents. The Jaccard similarity measure is used to compute bibliographic and conceptual coupling matrices for different sets of research papers. The results show that even though bibliographic coupling is widely used to assess relationships between research papers, it often falls short of identifying actual conceptual similarities within documents. This study’s findings carry important implications for areas such as information retrieval, interdisciplinary research and evaluation metrics, calling for a more refined understanding of how research documents relate to one another beyond their shared references.
Published in: Journal of Scientometric Research
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