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Emerging approaches in literature-based discovery: techniques and performance review

Published online by Cambridge University Press:  16 May 2017

Yakub Sebastian Open the ORCID record for Yakub Sebastian [Opens in a new window] ,
Eu-Gene Siew  and
Sylvester O. Orimaye
Yakub Sebastian
Affiliation:
Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia e-mail: ysebastian@swinburne.edu.my
Eu-Gene Siew
Affiliation:
School of Business, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Selangor, Malaysiae-mail: siew.eu-gene@monash.edu
Sylvester O. Orimaye
Affiliation:
School of Information Technology, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500 Subang Jaya, Selangor, Malaysiae-mail: sylvester.orimaye@monash.edu
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Abstract

Literature-based discovery systems aim at discovering valuable latent connections between previously disparate research areas. This is achieved by analyzing the contents of their respective literatures with the help of various intelligent computational techniques. In this paper, we review the progress of literature-based discovery research, focusing on understanding their technical features and evaluating their performance. The present literature-based discovery techniques can be divided into two general approaches: the traditional approach and the emerging approach. The traditional approach, which dominate the current research landscape, comprises mainly of techniques that rely on utilizing lexical statistics, knowledge-based and visualization methods in order to address literature-based discovery problems. On the other hand, we have also observed the births of new trends and unprecedented paradigm shifts among the recently emerging literature-based discovery approach. These trends are likely to shape the future trajectory of the next generation literature-based discovery systems.

Type
Survey Article
Information
The Knowledge Engineering Review , Volume 32 , 2017 , e12
Copyright
© Cambridge University Press, 2017 

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