  Link prediction for knowledge graphs aims to predict missing connections
between entities. Prevailing methods are limited to a transductive setting and
hard to process unseen entities. The recent proposed subgraph-based models
provided alternatives to predict links from the subgraph structure surrounding
a candidate triplet. However, these methods require abundant known facts of
training triplets and perform poorly on relationships that only have a few
triplets. In this paper, we propose Meta-iKG, a novel subgraph-based
meta-learner for few-shot inductive relation reasoning. Meta-iKG utilizes local
subgraphs to transfer subgraph-specific information and learn transferable
patterns faster via meta gradients. In this way, we find the model can quickly
adapt to few-shot relationships using only a handful of known facts with
inductive settings. Moreover, we introduce a large-shot relation update
procedure to traditional meta-learning to ensure that our model can generalize
well both on few-shot and large-shot relations. We evaluate Meta-iKG on
inductive benchmarks sampled from NELL and Freebase, and the results show that
Meta-iKG outperforms the current state-of-the-art methods both in few-shot
scenarios and standard inductive settings.
