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HousE: Knowledge Graph Embedding with Householder Parameterization

Rui Li · Jianan Zhao · Chaozhuo Li · Di He · Yiqi Wang · Yuming Liu · Hao Sun · Senzhang Wang · Weiwei Deng · Yanming Shen · Xing Xie · Qi Zhang

Hall E #404

Keywords: [ DL: Graph Neural Networks ]


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

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