  Due to the lack of structured knowledge applied in learning distributed
representation of cate- gories, existing work cannot incorporate category
hierarchies into entity information. We propose a framework that embeds
entities and categories into a semantic space by integrating structured
knowledge and taxonomy hierarchy from large knowledge bases. The framework
allows to com- pute meaningful semantic relatedness between entities and
categories. Our framework can han- dle both single-word concepts and
multiple-word concepts with superior performance on concept categorization and
yield state of the art results on dataless hierarchical classification.
