Analyzing the impact of entities within their field is fundamental to understand it. To this end, it is essential to have fine numerical indicators retranscribing the specificities of the field. This paper proposes a transdisciplinary approach to automatically discover ranking scores having nevertheless an intradisciplinary semantics. For this purpose, our approach is based on the knowledge bases of the Web of data, not only to facilitate the operational computation of the indicators but also to take advantage of their transparency and their semantic richness. The simple but central hypothesis of this work is that each unequal distribution of a quantity generates a relevant ranking score. To this end, we use the Gini coefficient to identify in Wikidata the properties producing significant ranking scores.