Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
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.
Code (1)
Tasks
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsSimilar Papers 제목 키워드 기반
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts. While rotation-based methods like RotatE and QuatE perform well in K…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsRelation+1Universal Knowledge Graph Embeddings
A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting. As a result, the embeddings reflect…
Entity DisambiguationGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG). While the majority of approaches concentrate only on relational information, i.e., relations between entities, fewer a…
Knowledge Graph EmbeddingsKnowledge GraphsConvolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as socia…
Node ClassificationSkeleton Based Action RecognitionOrthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE. However, N-1, 1-N and N-N predictions still remain…
Graph EmbeddingKnowledge Graph EmbeddingLink PredictionPrediction+1