  Link prediction is an important way to complete knowledge graphs (KGs), while
embedding-based methods, effective for link prediction in KGs, perform poorly
on relations that only have a few associative triples. In this work, we propose
a Meta Relational Learning (MetaR) framework to do the common but challenging
few-shot link prediction in KGs, namely predicting new triples about a relation
by only observing a few associative triples. We solve few-shot link prediction
by focusing on transferring relation-specific meta information to make model
learn the most important knowledge and learn faster, corresponding to relation
meta and gradient meta respectively in MetaR. Empirically, our model achieves
state-of-the-art results on few-shot link prediction KG benchmarks.
