We propose Dynamically Pruned Message Passing Networks (DPMPN) for
large-scale knowledge graph reasoning. In contrast to existing models,
embedding-based or path-based, we learn an input-dependent subgraph to
explicitly model reasoning process. Subgraphs are dynamically constructed and
expanded by applying graphical attention mechanism conditioned on input
queries. In this way, we not only construct graph-structured explanations but
also enable message passing designed in Graph Neural Networks (GNNs) to scale
with graph sizes. We take the inspiration from the consciousness prior proposed
by and develop a two-GNN framework to simultaneously encode input-agnostic full
graph representation and learn input-dependent local one coordinated by an
attention module. Experiments demonstrate the reasoning capability of our model
that is to provide clear graphical explanations as well as deliver accurate
predictions, outperforming most state-of-the-art methods in knowledge base
completion tasks.

本研究提出了基于动态修剪的消息传递网络 (DPMPN) 来进行大规模知识图谱推理，该模型使用图形式的注意力机制来动态构造和扩展子图，并且能够对输入进行建模，可同时提供图形式的解释和准确的预测，并在知识库完成任务中胜过大部分现有方法。