S-Paths: Set-Based Visual Exploration of Linked Data Driven by Semantic Paths

Tracking #: 2195-3408

This paper is currently under review
Authors: 
Marie Destandau
Caroline Appert
Emmanuel Pietriga

Responsible editor: 
Claudia d'Amato

Submission type: 
Full Paper
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 investigate a navigation strategy based on semantic paths and aggregation. Starting from sets of resources, we follow chains of triples (semantic paths) until we find properties that 1) provide meaningful descriptions of resources in those sets, and 2) are amenable to visual representation, considering a broad range of visualization techniques. We implement this approach in \spaths{}, a browsing tool for linked datasets that systematically tries to identify the most relevant 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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Under Review