  A large amount of data accommodated in knowledge graphs (KG) is actually
metric. For example, the Wikidata KG contains a plenitude of metric facts about
geographic entities like cities, chemical compounds or celestial objects. In
this paper, we propose a novel approach that transfers orometric (topographic)
measures to bounded metric spaces. While these methods were originally designed
to identify relevant mountain peaks on the surface of the earth, we demonstrate
a notion to use them for metric data sets in general. Notably, metric sets of
items inclosed in knowledge graphs. Based on this we present a method for
identifying outstanding items using the transferred valuations functions
'isolation' and 'prominence'. Building up on this we imagine an item
recommendation process. To demonstrate the relevance of the novel valuations
for such processes we use item sets from the Wikidata knowledge graph. We then
evaluate the usefulness of 'isolation' and 'prominence' empirically in a
supervised machine learning setting. In particular, we find structurally
relevant items in the geographic population distributions of Germany and
France.

Artificial Intelligence;Machine Learning;Machine Learning