Machine Learning Algorithms for Big Scholarly Data: A Novel Approach

Authors

  • Raghavendra Nayaka P, Rajeev Ranjan

Abstract

With the fast growth of publishing the research data, managing and analyzing the scholarly data has been challenging for the researchers. The term big scholarly data contains all the information including several research papers published by various authors over the globe, these research papers includes citations ,figures, tables etc., as well as scholarly networks and digital libraries. This paper presents a on how the techniques of the machine learning can be applied over Big Scholarly data which is much needed work for maintain quality and analysis over scholarly data. This review paper also provides a critical analysis of the work on what exactly this Big Scholarly Data is, by comparing with various methods along with existing research problems. Then the review focuses on Machine learning techniques, framework comparison and various algorithm used in the existing research work. Finally the works concentrates on applications of Machine Learning over Big Scholarly Data, along with the challenges faced in the existing work with how Machine Learning techniques can be applied over Big Scholarly Data and can be processed further is discussed.

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Published

2020-05-12

Issue

Section

Articles