Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Ranking Entity Facets Based on User Click Feedback
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
When entities meet query recommender systems: semantic search shortcuts
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Exploiting user clicks for automatic seed set generation for entity matching
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.