  Relation reasoning in knowledge graphs (KGs) aims at predicting missing
relations in incomplete triples, whereas the dominant paradigm is learning the
embeddings of relations and entities, which is limited to a transductive
setting and has restriction on processing unseen entities in an inductive
situation. Previous inductive methods are scalable and consume less resource.
They utilize the structure of entities and triples in subgraphs to own
inductive ability. However, in order to obtain better reasoning results, the
model should acquire entity-independent relational semantics in latent rules
and solve the deficient supervision caused by scarcity of rules in subgraphs.
To address these issues, we propose a novel graph convolutional network
(GCN)-based approach for interpretable inductive reasoning with relational path
contrast, named RPC-IR. RPC-IR firstly extracts relational paths between two
entities and learns representations of them, and then innovatively introduces a
contrastive strategy by constructing positive and negative relational paths. A
joint training strategy considering both supervised and contrastive information
is also proposed. Comprehensive experiments on three inductive datasets show
that RPC-IR achieves outstanding performance comparing with the latest
inductive reasoning methods and could explicitly represent logical rules for
interpretability.
