Ranking Knowledge Graphs By Capturing Knowledge about Languages and Labels

Capturing knowledge about the mulitilinguality of a knowledge graph is of supreme importance to understand its applicability across multiple languages. Several metrics have been proposed for describing mulitilinguality at the level of a whole knowledge graph. Albeit enabling the understanding of the ecosystem of knowledge graphs in terms of the utilized languages, they are unable to capture a fine-grained description of the languages in which the different entities and properties of the knowledge graph are represented. This lack of representation prevents the comparison of existing knowl- edge graphs in order to decide which are the most appropriate for a multilingual application. In this work, we approach the problem of ranking knowledge graphs based on their language features and propose LINGVO, a framework able to capture mulitilinguality at different levels of granularity. Grounded on knowledge graph de- scriptions, LINGVO is, additionally, able to solve the problem of ranking knowledge graphs according to degree of mulitilingual- ity of the represented entities. We have empirically studied the effectiveness of LINGVO in a benchmark of queries to be executed against existing knowledge graphs. The observed results provide evidence that LINGVO captures the mulitilinguality of the studied knowledge graphs similarly than a crowd-sourced gold standard.