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.

Salatino, A., Osborne, F., Motta, E. (2018). AUGUR: Forecasting the Emergence of New Research Topics. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (pp.303-312). Institute of Electrical and Electronics Engineers Inc. [10.1145/3197026.3197052].

AUGUR: Forecasting the Emergence of New Research Topics

Osborne F;
2018

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.
paper
clustering algorithms; embryonic topic; ontologies; scholarly data; semantic technologies; topic detection; topic trends;
English
18th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2018 - 3 June 2018 through 7 June 2018
2018
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
978-145035178-2
2018
303
312
https://dl.acm.org/doi/abs/10.1145/3197026.3197052
none
Salatino, A., Osborne, F., Motta, E. (2018). AUGUR: Forecasting the Emergence of New Research Topics. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (pp.303-312). Institute of Electrical and Electronics Engineers Inc. [10.1145/3197026.3197052].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/381511
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