  Capturing associations for knowledge graphs (KGs) through entity alignment,
entity type inference and other related tasks benefits NLP applications with
comprehensive knowledge representations. Recent related methods built on
Euclidean embeddings are challenged by the hierarchical structures and
different scales of KGs. They also depend on high embedding dimensions to
realize enough expressiveness. Differently, we explore with low-dimensional
hyperbolic embeddings for knowledge association. We propose a hyperbolic
relational graph neural network for KG embedding and capture knowledge
associations with a hyperbolic transformation. Extensive experiments on entity
alignment and type inference demonstrate the effectiveness and efficiency of
our method.
