@inproceedings{li2022house,
author = {Li, Rui and Li, Chaozhuo and Zhao, Jianan and He, Di and Wang, Yiqi and Liu, Yuming and Sun, Hao and Wang, Senzhang and Deng,  Weiwei and Shen, Yanming and Xie, Xing and Zhang, Qi},
title = {HousE: Knowledge Graph Embedding with Householder Parameterization},
booktitle = {ICML 2022},
year = {2022},
month = {May},
abstract = {The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and map- ping 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 in- volves a novel parameterization based on two kinds of Householder transformations: (1) Householder rotations to achieve superior ca- pacity 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/house-knowledge-graph-embedding-with-householder-parameterization/},
}