Thematic clustering and exploration of linked data

Abstract

Now that a huge amount of data is available in the Linked Data Cloud, the need of effective exploration and visualization techniques is becoming more and more important. In this paper, we propose aggregation and abstraction techniques for thematic clustering and exploration of linked data. These techniques transform a basic, flat view of a potentially large set of messy linked data for a given search target, into a high-level, thematic view called inCloud. In an inCloud, thematic exploration is guided by few essentials auto-describing their prominence for the search target and by their reciprocal proximity relations. © Springer-Verlag Berlin Heidelberg 2012.