Recent years have witnessed increasing interest in few-shot knowledge graph
completion (FKGC), which aims to infer unseen query triples for a few-shot
relation using a few reference triples about the relation. The primary focus of
existing FKGC methods lies in learning relation representations that can
reflect the common information shared by the query and reference triples. To
this end, these methods learn entity-pair representations from the direct
neighbors of head and tail entities, and then aggregate the representations of
reference entity pairs. However, the entity-pair representations learned only
from direct neighbors may have low expressiveness when the involved entities
have sparse direct neighbors or share a common local neighborhood with other
entities. Moreover, merely modeling the semantic information of head and tail
entities is insufficient to accurately infer their relational information
especially when they have multiple relations. To address these issues, we
propose a Relation-Specific Context Learning (RSCL) framework, which exploits
graph contexts of triples to learn global and local relation-specific
representations for few-shot relations. Specifically, we first extract graph
contexts for each triple, which can provide long-term entity-relation
dependencies. To encode the extracted graph contexts, we then present a
hierarchical attention network to capture contextualized information of triples
and highlight valuable local neighborhood information of entities. Finally, we
design a hybrid attention aggregator to evaluate the likelihood of the query
triples at the global and local levels. Experimental results on two public
datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods.

本文提出了一种关系特定的上下文学习框架，通过提取图上的上下文信息和设计分层注意力网络和注意力池化器，可以全局和局部地学习表示来进行 FKGC 任务，实验结果证明了其优于现有的方法。