  Knowledge graphs (KGs) serve as useful resources for various natural language
processing applications. Previous KG completion approaches require a large
number of training instances (i.e., head-tail entity pairs) for every relation.
The real case is that for most of the relations, very few entity pairs are
available. Existing work of one-shot learning limits method generalizability
for few-shot scenarios and does not fully use the supervisory information;
however, few-shot KG completion has not been well studied yet. In this work, we
propose a novel few-shot relation learning model (FSRL) that aims at
discovering facts of new relations with few-shot references. FSRL can
effectively capture knowledge from heterogeneous graph structure, aggregate
representations of few-shot references, and match similar entity pairs of
reference set for every relation. Extensive experiments on two public datasets
demonstrate that FSRL outperforms the state-of-the-art.
