  In knowledge graph completion (KGC), predicting triples involving emerging
entities and/or relations, which are unseen when the KG embeddings are learned,
has become a critical challenge. Subgraph reasoning with message passing is a
promising and popular solution. Some recent methods have achieved good
performance, but they (i) usually can only predict triples involving unseen
entities alone, failing to address more realistic fully inductive situations
with both unseen entities and unseen relations, and (ii) often conduct message
passing over the entities with the relation patterns not fully utilized. In
this study, we propose a new method named RMPI which uses a novel Relational
Message Passing network for fully Inductive KGC. It passes messages directly
between relations to make full use of the relation patterns for subgraph
reasoning with new techniques on graph transformation, graph pruning,
relation-aware neighborhood attention, addressing empty subgraphs, etc., and
can utilize the relation semantics defined in the ontological schema of KG.
Extensive evaluation on multiple benchmarks has shown the effectiveness of
techniques involved in RMPI and its better performance compared with the
existing methods that support fully inductive KGC. RMPI is also comparable to
the state-of-the-art partially inductive KGC methods with very promising
results achieved. Our codes and data are available at
https://github.com/zjukg/RMPI.
