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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.<\/jats:p>","DOI":"10.3233\/sw-200383","type":"journal-article","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T13:02:10Z","timestamp":1600434130000},"page":"99-116","source":"Crossref","is-referenced-by-count":4,"title":["S-Paths: Set-based visual exploration of linked data driven by semantic paths"],"prefix":"10.1177","volume":"12","author":[{"given":"Marie","family":"Destandau","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CNRS, Inria, LRI, France. 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