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. © 2013 Springer-Verlag.

Osborne, F., Motta, E., & Mulholland, P. (2013). Exploring scholarly data with Rexplore. In The Semantic Web – ISWC 2013. ISWC 2013 (pp.460-477). Springer, Berlin, Heidelberg [10.1007/978-3-642-41335-3_29].

Exploring scholarly data with Rexplore

Osborne F;
2013

Abstract

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. © 2013 Springer-Verlag.
Si
paper
Scientifica
Data Exploration; Data Integration; Data Mining; Empirical Evaluation; Ontology Population; Scholarly Data; Visual Analytics;
English
12th International Semantic Web Conference, ISWC 2013 - 21 October 2013 through 25 October 2013
978-364241334-6
https://link.springer.com/chapter/10.1007/978-3-642-41335-3_29
Osborne, F., Motta, E., & Mulholland, P. (2013). Exploring scholarly data with Rexplore. In The Semantic Web – ISWC 2013. ISWC 2013 (pp.460-477). Springer, Berlin, Heidelberg [10.1007/978-3-642-41335-3_29].
Osborne, F; Motta, E; Mulholland, P
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/381581
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