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.

中文翻译：

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学术数据挖掘：对其应用程序的系统评价

在过去的几十年中，学术网络和数字图书馆的广泛发展导致以各种形式（例如作者，论文，引文，会议和期刊）公开获得的学术数据激增。这引起了对大学术数据分析领域的兴趣，该领域从不同角度分析了科学发现在全球范围内的传播。尽管大学术数据的研究相对较新，但已经出现了一些有关如何研究不同学科中学术数据使用情况的研究。这些研究激发了对通过学术技术（例如学术网络和数字图书馆）生成的学术数据的研究，以构建可伸缩的方法来检索，推荐和分析学术内容。我们已经按照系统的方法对这些研究进行了分析，根据文献特征将它们分为不同的应用，并重点介绍了用于此目的的机器学习技术。我们还讨论了尚未解决的挑战，这些挑战尚未解决，无法促进学术数据挖掘领域的未来研究。