MQuinE: a cure for "Z-paradox" in knowledge graph embedding models

• URL: http://arxiv.org/abs/2402.03583v2
• Date: Wed, 7 Feb 2024 03:03:06 GMT
• Title: MQuinE: a cure for "Z-paradox" in knowledge graph embedding models
• Authors: Yang Liu, Huang Fang, Yunfeng Cai, Mingming Sun
• Abstract summary: 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 emphZ-paradox Motivated by the existence of Z-paradox, we propose a new KGE model called emphMQuinE that does not suffer from Z-paradox.
• Score: 31.859851141024357
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \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.

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