Now that a huge amount of data is available in the Linked Data Cloud, providing techniques for its effective exploration is becoming more and more important. In this paper, we propose aggregation and abstraction techniques for thematic 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.