  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.
