Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths. However, the former approach faces challenges in the presence of many relations (e.g., knowledge graphs), while the latter requires substantial domain expertise to identify relevant meta-paths. In this work we propose a novel approach to learn meta-paths and meta-path GNNs that are highly accurate based on a small number of informative meta-paths. Key element of our approach is a scoring function for measuring the potential informativeness of a relation in the incremental construction of the meta-path. Our experimental evaluation shows that the approach manages to correctly identify relevant meta-paths even with a large number of relations, and substantially outperforms existing multi-relational GNNs on synthetic and real-world experiments.

翻译：现有的大多数多关系图神经网络采用以下两种策略之一来识别有信息量的关系：要么将问题简化为低层权重学习，要么依赖手工构建的关系依赖链（即元路径）。然而，前者在存在大量关系（例如知识图谱）时面临挑战，而后者需要大量领域专业知识才能识别相关的元路径。本文提出一种新颖方法以学习元路径及基于元路径的图神经网络，该方法通过少量有信息量的元路径即可实现高精度。本方法的核心要素是一个评分函数，用于在元路径的增量构建过程中衡量关系潜在的信息量。实验评估表明，即使在关系数量庞大的情况下，该方法也能正确识别相关元路径，并在合成数据集和真实数据集实验上显著优于现有多关系图神经网络。