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        <title>MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding</title>
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    <abstract>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 Z-paradox. Motivated by the existence of Z-paradox, we propose a new KGE model called 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.</abstract>
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    <identifier type="doi">10.18653/v1/2024.emnlp-main.549</identifier>
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%0 Conference Proceedings
%T MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding
%A Liu, Yang
%A Fang, Huang
%A Cai, Yunfeng
%A Sun, Mingming
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F liu-etal-2024-mquine
%X 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 Z-paradox. Motivated by the existence of Z-paradox, we propose a new KGE model called 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.
%R 10.18653/v1/2024.emnlp-main.549
%U https://aclanthology.org/2024.emnlp-main.549/
%U https://doi.org/10.18653/v1/2024.emnlp-main.549
%P 9837-9850

Markdown (Informal)

[MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding](https://aclanthology.org/2024.emnlp-main.549/) (Liu et al., EMNLP 2024)

• MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding (Liu et al., EMNLP 2024)

ACL

• Yang Liu, Huang Fang, Yunfeng Cai, and Mingming Sun. 2024. MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9837–9850, Miami, Florida, USA. Association for Computational Linguistics.