MQuinE: a cure for "Z-paradox" in knowledge graph embedding models
<|reference_start|>MQuinE: a cure for "Z-paradox" in knowledge graph embedding models: Knowledge graph embedding (KGE) models achieved state-of-the-art results on many knowledge graph tasks including link prediction and information retrieval. Despite the superior performance of KGE models in practice, we discover a deficiency in the expressiveness of some popular existing KGE models called \emph{Z-paradox}. Motivated by the existence of Z-paradox, we propose a new KGE model called \emph{MQuinE} that does not suffer from Z-paradox while preserves strong expressiveness to model various relation patterns including symmetric/asymmetric, inverse, 1-N/N-1/N-N, and composition relations with theoretical justification. Experiments on real-world knowledge bases indicate that Z-paradox indeed degrades the performance of existing KGE models, and can cause more than 20\% accuracy drop on some challenging test samples. Our experiments further demonstrate that MQuinE can mitigate the negative impact of Z-paradox and outperform existing KGE models by a visible margin on link prediction tasks.<|reference_end|>
@article{liu2024mquine:,
    title={MQuinE: a cure for "Z-paradox" in knowledge graph embedding models},
    author={Yang Liu, Huang Fang, Yunfeng Cai, Mingming Sun},
    journal={arXiv preprint arXiv:2402.03583},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03583},
    primaryClass={cs.SI cs.AI cs.LG}
}