揭示知识图谱基础模型的表达能力受限于关系模式，提出更优设计。
How Expressive are Knowledge Graph Foundation Models?
- 基于三元组间关系交互的复杂模式提升表达力
- 新模型在多领域数据集上显著优于传统方法
- 为模型设计提供理论依据，适合图神经网络研究者
知识图谱基础模型（KGFMs）是知识图谱深度学习的前沿方向，能泛化至具有不同关系词汇的新知识图谱。尽管其经验表现优异，但理论理解仍十分有限。本文系统研究了KGFMs的表达能力，发现其强弱直接取决于所使用的图谱模式（motifs）。现有方法多依赖二元模式（即关系对之间的交互），限制了模型表达能力。为此，我们设计了基于更丰富模式（如三元关系交互）的新型KGFMs。实验证明，使用更复杂模式的模型在多个跨领域数据集上均取得更好性能，验证了理论分析的有效性。
原文摘要 · Abstract (English)
Knowledge Graph Foundation Models (KGFMs) are at the frontier for deep learning on knowledge graphs (KGs), as they can generalize to completely novel knowledge graphs with different relational vocabularies. Despite their empirical success, our theoretical understanding of KGFMs remains very limited. In this paper, we conduct a rigorous study of the expressive power of KGFMs. Specifically, we show that the expressive power of KGFMs directly depends on the motifs that are used to learn the relation representations. We then observe that the most typical motifs used in the existing literature are binary, as the representations are learned based on how pairs of relations interact, which limits the model's expressiveness. As part of our study, we design more expressive KGFMs using richer motifs, which necessitate learning relation representations based on, e.g., how triples of relations interact with each other. Finally, we empirically validate our theoretical findings, showing that the use of richer motifs results in better performance on a wide range of datasets drawn from different domains.
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