Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges

Yu Zhang (), Min Wang (), Morteza Saberi () and Elizabeth Chang ()
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Yu Zhang: UNSW Canberra
Min Wang: UNSW Canberra
Morteza Saberi: University of Technology Sydney
Elizabeth Chang: UNSW Canberra

Scientometrics, 2020, vol. 125, issue 3, No 34, 2637-2666

Abstract: Abstract The ever growing volume of academic articles stresses the need for a new generation of knowledge management method to intelligently reuse the academic knowledge and facilitate the development of scientific research. Knowledge fusion (KF) serves a key element of such method addressing those needs, and breakthrough progress has taken place in the field of KF. This brings a great opportunity for the academic community to expedite the process of literature review and automatically retrieve the required knowledge from academic publications. Therefore, a survey reviewing the KF studies in terms of the related technologies and applications for valuable insights to reuse academic knowledge, which is missing from the state-of-the-art literature, is in need. Motivated to bridge this gap, this paper conducts a systematic survey reviewing the existing studies on KF, meanwhile discussing the opportunities and challenges of applying KF through academic articles. To this end, we revisit the definitions of knowledge and KF in the context of academic articles, and summarise the fusion patterns and their usage in existing applications. Furthermore, we review the techniques and applications of KF, especially those with academic articles as sources of knowledge. Finally, we discuss the challenges and future directions in order to bring new insights to researchers and practitioners to deepen their understanding of knowledge fusion and to develop versatile functions.

Keywords: Knowledge fusion; Knowledge retrieval; Knowledge management; Data mining; Information retrieval (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11192-020-03683-3

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