Abstract During the last few decades, the widespread growth of scholarly networks and digital libraries has resulted in an explosion of publicly available scholarly data in various forms such as authors, papers, citations, conferences, and journals. This has created interest in the domain of big scholarly data analysis that analyses worldwide dissemination of scientific findings from different perspectives. Although the study of big scholarly data is relatively new, some studies have emerged on how to investigate scholarly data usage in different disciplines. These studies motivate investigating the scholarly data generated via academic technologies such as scholarly networks and digital libraries for building scalable approaches for retrieving, recommending, and analyzing the scholarly content. We have analyzed these studies following a systematic methodology, classifying them into different applications based on literature features and highlighting the machine learning techniques used for this purpose. We also discuss open challenges that remain unsolved to foster future research in the field of scholarly data mining. This article is categorized under: Algorithmic Development > Text Mining Application Areas > Science and Technology
Stored only when both papers are in the corpus — 23 citations exist upstream.
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