3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding
This work addresses the problem of improving knowledge graph embeddings for AI applications by simultaneously capturing complex relation patterns, representing an incremental advancement over prior methods.
The authors tackled the challenge of capturing multiple relation patterns in knowledge graph embeddings by introducing 3H-TH, a model using 3D rotation and translation in hyperbolic space, which outperformed state-of-the-art models in accuracy and hierarchy in low-dimensional space.
The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts. A significant challenge in achieving better KG embeddings lies in capturing relation patterns, including symmetry, antisymmetry, inversion, commutative composition, non-commutative composition, hierarchy, and multiplicity. This study introduces a novel model called 3H-TH (3D Rotation and Translation in Hyperbolic space) that captures these relation patterns simultaneously. In contrast, previous attempts have not achieved satisfactory performance across all the mentioned properties at the same time. The experimental results demonstrate that the new model outperforms existing state-of-the-art models in terms of accuracy, hierarchy property, and other relation patterns in low-dimensional space, meanwhile performing similarly in high-dimensional space.