DataType-Aware Knowledge Graph Representation Learning in Hyperbolic Space

Abstract

Knowledge Graph (KG) representation learning aims to encode both entities and relations into a continuous low-dimensional vector space. Most existing methods only concentrate on learning representations from structural triples in Euclidean space, which cannot well exploit the rich semantic information with hierarchical structure in KGs. In this paper, we propose a novel DataType-aware hyperbolic knowledge representation learning model called DT-GCN, which has the advantage of fully embedding attribute values of data types information. We refine data types into five primitive modalities, including integer, double, Boolean, temporal, and textual. For each modality, an encoder is specifically designed to learn its embedding. In addition, we define a unified space based on Euclidean, spherical, and hyperbolic space, which is a continuous curvature space that combines advantages of three different spaces. Extensive experiments on both synthetic and real-world datasets show that our model is consistently better than the state-of-the-art models. The average performance is improved by 2.19% and 3.46% than the optimal baseline model on node classification and link prediction tasks, respectively. The results of ablation experiments demonstrate the advantages of embedding data types information and leveraging the unified space.