  Due to the lack of structured knowledge applied in learning distributed
representation of categories, 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 compute meaningful semantic relatedness between entities and
categories.~Compared with the previous state of the art, our framework can
handle both single-word concepts and multiple-word concepts with superior
performance in concept categorization and semantic relatedness.
