Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC). Although bilinear based models have achieved significant advances, these studies mainly concentrate on posterior properties (based on evidence, e.g. symmetry pattern) while neglecting the prior properties. In this paper, we find a prior property named "the law of identity" that cannot be captured by bilinear based models, which hinders them from comprehensively modeling the characteristics of KGs. To address this issue, we introduce a solution called Unit Ball Bilinear Model (UniBi). This model not only achieves theoretical superiority but also offers enhanced interpretability and performance by minimizing ineffective learning through minimal constraints. Experiments demonstrate that UniBi models the prior property and verify its interpretability and performance.

翻译：双线性模型是知识图谱补全（KGC）中强大且广泛使用的方法。尽管双线性模型取得了显著进展，但这些研究主要集中于后验属性（基于证据，如对称模式），而忽视了先验属性。本文发现一种称为"同一律"的先验属性无法被双线性模型捕获，这阻碍了模型全面建模知识图谱的特性。为解决该问题，我们提出一种称为单位球双线性模型（UniBi）的解决方案。该模型不仅具有理论优越性，还能通过最小化约束来减少无效学习，从而增强可解释性和性能。实验表明，UniBi能够建模该先验属性，并验证了其可解释性和性能。