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S-Paths: Set-based visual exploration of linked data driven by semantic paths

Marie Destandau 1 Caroline Appert 1 Emmanuel Pietriga 1 
1 ILDA - Interacting with Large Data
Inria Saclay - Ile de France, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, IaH - Interaction avec l'Humain
Abstract : Meaningful information about an RDF resource can be obtained not only by looking at its properties, but by putting it in the broader context of similar resources. Classic navigation paradigms on the Web of Data that employ a follow-your-nose strategy fail to provide such context, and put strong emphasis on first-level properties, forcing users to drill down in the graph one step at a time. We introduce the concept of semantic paths: starting from a set of resources, we follow and analyse chains of triples and characterize the sets of values at their end. We investigate a navigation strategy based on aggregation, relying on path characteristics to determine the most readable representation. We implement this approach in S-Paths, a browsing tool for linked datasets that systematically identifies the best rated view on a given resource set, leaving users free to switch to another resource set, or to get a different perspective on the same set by selecting other semantic paths to visualize.
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Submitted on : Thursday, October 8, 2020 - 9:23:49 AM
Last modification on : Friday, April 8, 2022 - 3:38:34 AM
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Marie Destandau, Caroline Appert, Emmanuel Pietriga. S-Paths: Set-based visual exploration of linked data driven by semantic paths. Open Journal Of Semantic Web, Research Online Publishing (RonPub), 2021, 12 (1), pp.99-116. ⟨10.3233/SW-200383⟩. ⟨hal-02960913⟩



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