  Graph neural networks (GNNs) have shown promising performance for knowledge
graph reasoning. A recent variant of GNN called progressive relational graph
neural network (PRGNN), utilizes relational rules to infer missing knowledge in
relational digraphs and achieves notable results. However, during reasoning
with PRGNN, two important properties are often overlooked: (1) the
sequentiality of relation composition, where the order of combining different
relations affects the semantics of the relational rules, and (2) the lagged
entity information propagation, where the transmission speed of required
information lags behind the appearance speed of new entities. Ignoring these
properties leads to incorrect relational rule learning and decreased reasoning
accuracy. To address these issues, we propose a novel knowledge graph reasoning
approach, the Relational rUle eNhanced Graph Neural Network (RUN-GNN).
Specifically, RUN-GNN employs a query related fusion gate unit to model the
sequentiality of relation composition and utilizes a buffering update mechanism
to alleviate the negative effect of lagged entity information propagation,
resulting in higher-quality relational rule learning. Experimental results on
multiple datasets demonstrate the superiority of RUN-GNN is superior on both
transductive and inductive link prediction tasks.
