document: Ranking related entities for web search queries Entity ranking is a recent paradigm that refers to retrieving and ranking related objects and entities from different structured sources in various scenarios . Entities typically have associated categories and relationships with other entities . In this work , we present an extensive analysis of Web-scale entity ranking , based on machine learned ranking models using an ensemble of pairwise preference models . Our proposed system for entity ranking uses structured knowledge bases , entity relationship graphs and user data to derive useful features to facilitate semantic search with entities directly within the learning to rank framework . The experimental results are validated on a large-scale graph containing millions of entities and hundreds of millions of entity relationships . We show that our proposed ranking solution clearly improves a simple user behavior based ranking model .
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