Reasoning on large-scale knowledge graphs has been long dominated by
embedding methods. While path-based methods possess the inductive capacity that
embeddings lack, they suffer from the scalability issue due to the exponential
number of paths. Here we present A*Net, a scalable path-based method for
knowledge graph reasoning. Inspired by the A* algorithm for shortest path
problems, our A*Net learns a priority function to select important nodes and
edges at each iteration, to reduce time and memory footprint for both training
and inference. The ratio of selected nodes and edges can be specified to trade
off between performance and efficiency. Experiments on both transductive and
inductive knowledge graph reasoning benchmarks show that A*Net achieves
competitive performance with existing state-of-the-art path-based methods,
while merely visiting 10% nodes and 10% edges at each iteration. On a
million-scale dataset ogbl-wikikg2, A*Net achieves competitive performance with
embedding methods and converges faster. To our best knowledge, A*Net is the
first path-based method for knowledge graph reasoning at such a scale.

本文介绍了一种基于路径的知识图谱推理方法 A*Net，通过学习一个优先级函数，选择重要节点和边，以在训练和推理中降低时间和内存开销，成功在大规模数据集上实现了高效的推理，是迄今为止第一个具有这种规模的基于路径的知识图谱推理方法。