HousE: Knowledge Graph Embedding with Householder Parameterization
This work addresses the need for more powerful knowledge graph embeddings, which is crucial for AI applications like question answering and recommendation systems, and it represents an incremental improvement over existing rotation-based models.
The paper tackles the problem of insufficient modeling capacity in knowledge graph embedding by proposing HousE, a framework using Householder transformations, which achieves new state-of-the-art performance on five benchmark datasets.
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework named HousE, which involves a novel parameterization based on two kinds of Householder transformations: (1) Householder rotations to achieve superior capacity of modeling relation patterns; (2) Householder projections to handle sophisticated relation mapping properties. Theoretically, HousE is capable of modeling crucial relation patterns and mapping properties simultaneously. Besides, HousE is a generalization of existing rotation-based models while extending the rotations to high-dimensional spaces. Empirically, HousE achieves new state-of-the-art performance on five benchmark datasets. Our code is available at https://github.com/anrep/HousE.