Learning hyperbolic attention-based embeddings for link prediction in knowledge graphs

Abstract

Knowledge graph (KG) embedding methods aim to learn low-dimensional representations of entities and relations to predict new valid triples for KG completion. Most of the existing KG embedding models learn embeddings in Euclidean space, which cannot accurately capture hierarchical structures and complex properties of relations found in KGs. To this effect, a recent model MuRP, as a first hyperbolic method, learns the KG embeddings in hyperbolic space and outperforms existing Euclidean embedding models. However, MuRP treats the KG triples individually, and hence fails to capture the complex structural information inherent in the local vicinity of a node, leading to low-quality node embeddings. On the other hand, the recent hyperbolic graph neural network (HGNN) provides a way of learning high-quality hyperbolic node embeddings by capturing information from each node’s neighborhood. However, HGNN ignores the relation features and treats all neighboring nodes with equal importance. To this end, we propose RHGNN, which extends HGNN by including the relation features and performing a hyperbolic attention-based neighborhood aggregation. We then combine RHGNN with MuRP into a novel encoder–decoder hyperbolic embedding learning framework (which we call HyperGEL) for KG completion. RHGNN gathers information from the neighboring nodes to generate rich hyperbolic node embeddings, and MuRP uses these embeddings to predict new triples. Experimental results show the proposed framework’s effectiveness that consistently marks performance gains over several previous models on recent standard KG completion datasets.