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Issue title: Visual exploration and analysis of Linked Data
Guest editors: Aba-Sah Dadzie and Emmanuel Pietriga
Article type: Research Article
Authors: Fu, Boa; * | Noy, Natalya F.b; ** | Storey, Margaret-Annea
Affiliations: [a] Department of Computer Science, University of Victoria, Canada | [b] Stanford Center for Biomedical Informatics Research, Stanford University, US
Correspondence: [*] Corresponding author. E-mail: [email protected].
Note: [**] Current affiliation: Google Inc.
Abstract: Various ontology visualization techniques have been developed over the years, offering essential interfaces to users for browsing and interacting with ontologies, in an effort to assist with ontology understanding. Yet few studies have focused on evaluating the usability of existing ontology visualization techniques. This paper presents an eye-tracking user study that evaluates two commonly used ontology visualization techniques, namely, indented list and graph. The eye-tracking experiment and analysis presented in this paper complements the set of existing evaluation protocols for ontology visualization. In addition, the results found from this study contribute to a greater understanding of the strengths and weaknesses of the two visualization techniques, and in particular, how and why one is more effective than the other. Based on approximately 500 MB of eye movement data containing around 30 million rows of gaze data generated by a Tobii eye tracker, we found evidence suggesting indented lists are more efficient at supporting information searches and graphs are more efficient at supporting information processing.
Keywords: Eye tracking, ontology visualization evaluation, usability study, gaze data processing, statistical analysis
DOI: 10.3233/SW-140163
Journal: Semantic Web, vol. 8, no. 1, pp. 23-41, 2017
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