def highest_relative_content(max_chunks = 10)
      return [] if needs_rebuild?

      avg_density = {}
      @items.each_key { |item| avg_density[item] = proximity_array_for_content(item).inject(0.0) { |x, y| x + y[1] } }

      avg_density.keys.sort_by { |x| avg_density[x] }.reverse[0..max_chunks - 1].map
    end
def highest_relative_content(max_chunks = 10)
      return [] if needs_rebuild?

      avg_density = {}
      @items.each_key { |item| avg_density[item] = proximity_array_for_content(item).inject(0.0) { |x, y| x + y[1] } }

      avg_density.keys.sort_by { |x| avg_density[x] }.reverse[0..max_chunks - 1].map
    end
This method returns max_chunks entries, ordered by their average semantic rating.
 Essentially, the average distance of each entry from all other entries is calculated,
 the highest are returned.

 This can be used to build a summary service, or to provide more information about
 your dataset's general content. For example, if you were to use categorize on the
 results of this data, you could gather information on what your dataset is generally
 about.