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AUGUR: Forecasting the Emergence of New Research Topics

Salatino, Angelo A.; Osborne, Francesco and Motta, Enrico (2018). AUGUR: Forecasting the Emergence of New Research Topics. In: JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, ACM, New York, NY, USA pp. 303–312.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3197026.3197052
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Abstract

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 The Authors
ISBN: 1-4503-5178-6, 978-1-4503-5178-2
Extra Information: Originally presented at JCDL ’18: The 18th ACM/IEEE Joint Conference on Digital Libraries, Fort Worth, TX, USA, 3-7 Jun 2018.
Keywords: scholarly data; embryonic topic; topic detection; topic trends; semantic technologies; clustering algorithms; ontologies
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 54345
Depositing User: Angelo Salatino
Date Deposited: 23 Apr 2018 08:12
Last Modified: 08 Dec 2018 04:01
URI: http://oro.open.ac.uk/id/eprint/54345
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