Global Entity Ranking Across Multiple Languages

Research paper by Prantik Bhattacharyya, Nemanja Spasojevic

Indexed on: 17 Mar '17Published on: 17 Mar '17Published in: arXiv - Computer Science - Information Retrieval


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.