Learning to rank related entities in Web search

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 pair-wise 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. We also describe a suite of novel features in the context of entity ranking and present a detailed feature space analysis. 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 simple user behavior based ranking and several baselines.