Existing distributed representations are limited in utilizing structured knowledge to improve semantic relatedness modeling. We propose a principled framework of embedding entities that integrates hierarchical information from large-scale knowledge bases. The novel embedding model associates each category node of the hierarchy with a distance metric. To capture structured semantics, the entity similarity of context prediction are measured under the aggregated metrics of relevant categories

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... all inter-entity paths. We show that both the entity vectors and category distance metrics encode meaningful semantics. Experiments in entity linking and entity search show superiority of the proposed method.