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Exploring Scholarly Data with Rexplore

  • Francesco Osborne
  • Enrico Motta
  • Paul Mulholland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8218)

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.

Keywords

Scholarly Data Visual Analytics Data Exploration Empirical Evaluation Ontology Population Data Mining Data Integration 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesco Osborne
    • 1
    • 2
  • Enrico Motta
    • 1
  • Paul Mulholland
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityUK
  2. 2.Dept. of Computer ScienceUniversity of TorinoTorinoItaly

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