Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification

Existing work learning distributed representations of knowledge base entities has largely failed to incorporate rich categorical structure, and is unable to induce category representations. We propose a new framework that embeds entities and categories jointly into a semantic space, by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. Our framework enables to compute meaningful semantic relatedness between entities and categories in a principled way, and can handle both single-word and multiple-word concepts. Our method shows significant improvement on the tasks of concept categorization and dataless hierarchical classification.