Global Entity Ranking Across Multiple Languages

We present work on building a global long-tailed ranking of entities across multiple languages using Wikipedia and Freebase knowledge bases. We identify multiple features and build a model to rank entities using a ground-truth dataset of more than 10 thousand labels. The final system ranks 27 million entities with 75 and empirical evidence of the quality of ranking across languages, and open the final ranked lists for future research.