  Few-shot Knowledge Graph (KG) completion is a focus of current research,
where each task aims at querying unseen facts of a relation given its few-shot
reference entity pairs. Recent attempts solve this problem by learning static
representations of entities and references, ignoring their dynamic properties,
i.e., entities may exhibit diverse roles within task relations, and references
may make different contributions to queries. This work proposes an adaptive
attentional network for few-shot KG completion by learning adaptive entity and
reference representations. Specifically, entities are modeled by an adaptive
neighbor encoder to discern their task-oriented roles, while references are
modeled by an adaptive query-aware aggregator to differentiate their
contributions. Through the attention mechanism, both entities and references
can capture their fine-grained semantic meanings, and thus render more
expressive representations. This will be more predictive for knowledge
acquisition in the few-shot scenario. Evaluation in link prediction on two
public datasets shows that our approach achieves new state-of-the-art results
with different few-shot sizes.
