Prior Bilinear Based Models for Knowledge Graph Completion

2023-09-25 · Jiayi Li, Ruilin Luo, Jiaqi Sun, Jing Xiao, Yujiu Yang

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.

📄 PDF Abstract BibTeX arXiv:2309.13834

Code (1)

lrlbbzl/unibi_ogb 공식 구현 pytorch

Tasks

Knowledge Graph Completion Knowledge Graphs

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