# Global Entity Ranking Across Multiple Languages

## TL;DR

This paper develops a multilingual entity ranking system leveraging Wikipedia and Freebase, achieving high precision and F1 scores across 27 million entities, facilitating future research in cross-lingual knowledge organization.

## Contribution

It introduces a novel model for global entity ranking across multiple languages using large-scale knowledge bases and a ground-truth dataset, with comprehensive performance evaluation.

## Key findings

- Ranks 27 million entities with 75% precision
- Achieves 48% F1 score in multilingual ranking
- Provides open access to ranked entity lists

## Abstract

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% precision and 48% F1 score. We provide performance evaluation and empirical evidence of the quality of ranking across languages, and open the final ranked lists for future research.