Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful applications. Nonetheless, it remains challenging to transfer the success of these architectures to relational hypergraphs, where the task of link prediction is over $k$-ary relations, which is substantially harder than link prediction with knowledge graphs. In this paper, we propose a framework for link prediction with relational hypergraphs, unlocking applications of graph neural networks to fully relational structures. Theoretically, we conduct a thorough analysis of the expressive power of the resulting model architectures via corresponding relational Weisfeiler-Leman algorithms and also via logical expressiveness. Empirically, we validate the power of the proposed model architectures on various relational hypergraph benchmarks. The resulting model architectures substantially outperform every baseline for inductive link prediction, and lead to state-of-the-art results for transductive link prediction.

翻译：知识图谱的链接预测在图机器学习领域已得到深入研究，催生了丰富的图神经网络架构及其成功应用。然而，将这些架构的成功经验迁移至关系超图仍面临挑战，因为在关系超图中链接预测任务面向的是$k$元关系，其难度显著高于知识图谱的链接预测。本文提出了一种基于关系超图的链接预测框架，为图神经网络在完全关系结构上的应用开辟了新途径。在理论层面，我们通过对应的关系Weisfeiler-Leman算法和逻辑表达能力，对所得模型架构的表达能力进行了深入分析。在实证层面，我们在多种关系超图基准测试中验证了所提模型架构的性能。实验结果表明，所得模型架构在归纳式链接预测任务上显著优于所有基线方法，并在传导式链接预测任务上取得了最先进的结果。