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Exploring scholarly data with Rexplore.

Osborne, Francesco; Motta, Enrico and Mulholland, Paul (2013). Exploring scholarly data with Rexplore. In: International Semantic Web Conference (ISWC 2013), 21-25 Oct 2013, Sydney, Australia, Springer, pp. 460–477.

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Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 Springer-Verlag
ISBN: 3-642-41334-X, 978-3-642-41334-6
ISSN: 0302-9743
Extra Information: 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, Proceedings, Part I
Editors: Harith Alani (et al.)
Lecture Notes in Computer Science, Vol.8218
Keywords: scholarly data; visual analytics; data exploration; empirical evaluation; ontology population; data mining; data integration
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 39160
Depositing User: Francesco Osborne
Date Deposited: 16 Dec 2013 10:09
Last Modified: 02 May 2019 10:49
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