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Journal Articles Open Journal Of Semantic Web Year : 2021

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

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Marie Destandau
Caroline Appert
Emmanuel Pietriga

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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Dates and versions

hal-02960913 , version 1 (08-10-2020)

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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, 2021, 12 (1), pp.99-116. ⟨10.3233/SW-200383⟩. ⟨hal-02960913⟩
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