We study Graph Neural Networks (GNNs)-based embedding techniques for
knowledge graph (KG) reasoning. For the first time, we link the path redundancy
issue in the state-of-the-art KG reasoning models based on path encoding and
message passing to the transformation error in model training, which brings us
new theoretical insights into KG reasoning, as well as high efficacy in
practice. On the theoretical side, we analyze the entropy of transformation
error in KG paths and point out query-specific redundant paths causing entropy
increases. These findings guide us to maintain the shortest paths and remove
redundant paths for minimized-entropy message passing. To achieve this goal, on
the practical side, we propose an efficient Graph Percolation Process motivated
by the percolation model in Fluid Mechanics, and design a lightweight GNN-based
KG reasoning framework called Graph Percolation Embeddings (GraPE). GraPE
outperforms previous state-of-the-art methods in both transductive and
inductive reasoning tasks while requiring fewer training parameters and less
inference time.

本文研究了基于图神经网络（GNN）的嵌入技术在知识图谱（KG）推理中的应用。第一次将路径编码和信息传递方法中的路径冗余问题，与模型训练中的转换误差联系起来，提出了一种新的 KG 推理理论和高效的实践方法，即通过最小化熵的信息传递来维护最短路径和消除冗余路径。作者还设计了一种受流体力学脉冲模型启发的高效的图注入过程，提出了一个轻量级的 GNN-based KG 推理框架 GraPE，实验结果表明 GraPE 在传导式和归纳式推理任务中都优于之前的最新方法，且需要更少的训练参数和推理时间。