Entity Hierarchy Embedding

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 along 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.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Zhiting Hu, Poyao Huang, Yuntian Deng, Yingkai Gao, Eric P. Xing
• Keywords: ACL 2015, Computational Linguistics