Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
This work addresses limitations in knowledge graph embedding for AI applications, offering a novel method that is incremental in improving modeling capability.
The authors tackled the problem of rigid relational orthogonalization in knowledge graph embedding by introducing GoldE, a framework using universal orthogonal parameterization based on Householder reflection, which achieved state-of-the-art performance on three standard benchmarks.
Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures. However, existing approaches are confined to rigid relational orthogonalization with restricted dimension and homogeneous geometry, leading to deficient modeling capability. In this work, we move beyond these approaches in terms of both dimension and geometry by introducing a powerful framework named GoldE, which features a universal orthogonal parameterization based on a generalized form of Householder reflection. Such parameterization can naturally achieve dimensional extension and geometric unification with theoretical guarantees, enabling our framework to simultaneously capture crucial logical patterns and inherent topological heterogeneity of knowledge graphs. Empirically, GoldE achieves state-of-the-art performance on three standard benchmarks. Codes are available at https://github.com/xxrep/GoldE.