Joint Embeddings of Hierarchical Categories and Entities Yuezhang Li Ronghuo Zheng Tian Tian Zhiting Hu Rahul Iyer Katia Sycara Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA 15213, USA
[email protected] Abstract the relatedness measure between entities and cat- egories, although they successfully learn entity Due to the lack of structured knowledge representations and relatedness measure between applied in learning distributed represen- entities (Hu et al., 2015). Knowledge graph em- tation of categories, existing work can- bedding methods (Wang et al., 2014; Lin et al., not incorporate category hierarchies into 2015) give embeddings of entities and relations entity information. We propose a frame- but lack category representations and relatedness work that embeds entities and categories measure. Though taxonomy embedding methods into a semantic space by integrating struc- (Weinberger and Chapelle, 2009; Hwang and Si- tured knowledge and taxonomy hierarchy gal, 2014) learn category embeddings, they pri- from large knowledge bases. The frame- marily target documents classification not entity work allows to compute meaningful se- representations. mantic relatedness between entities and In this paper, we propose two models to si- categories. Compared with the previous multaneously learn entity and category vectors state of the art, our framework can handle from large-scale knowledge bases (KBs). They both single-word concepts and multiple- are Category Embedding model and Hierarchi- word concepts with superior performance cal Category Embedding model. The Category in concept categorization and semantic re- Embedding model (CE model) extends the entity latedness. embedding method of (Hu et al., 2015) by using category information with entities to learn entity 1 Introduction and category embeddings.