  Despite their large-scale coverage, cross-domain knowledge graphs invariably
suffer from inherent incompleteness and sparsity. Link prediction can alleviate
this by inferring a target entity, given a source entity and a query relation.
Recent embedding-based approaches operate in an uninterpretable latent semantic
vector space of entities and relations, while path-based approaches operate in
the symbolic space, making the inference process explainable. However, these
approaches typically consider static snapshots of the knowledge graphs,
severely restricting their applicability for evolving knowledge graphs with
newly emerging entities. To overcome this issue, we propose an inductive
representation learning framework that is able to learn representations of
previously unseen entities. Our method finds reasoning paths between source and
target entities, thereby making the link prediction for unseen entities
interpretable and providing support evidence for the inferred link.
