Orometric Methods in Bounded Metric Data
This work addresses the need for item recommendation in metric data sets, particularly within knowledge graphs, but it is incremental as it adapts existing topographic methods to a new context.
The paper tackles the problem of identifying outstanding items in bounded metric data, such as those in knowledge graphs, by transferring orometric measures like isolation and prominence from topographic analysis. The result demonstrates the relevance of these valuations in a supervised machine learning setting, using geographic population distributions from Germany and France.
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.