Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge
  Graph Completion
Qiao Qiao Yuepei Li Kang Zhou Qi Li
Few-shot knowledge graph completion (FKGC) task aims to predict unseen facts
of a relation with few-shot reference entity pairs. Current approaches randomly
select one negative sample for each reference entity pair to minimize a
margin-based ranking loss, which easily leads to a zero-loss problem if the
negative sample is far away from the positive sample and then out of the
margin. Moreover, the entity should have a different representation under a
different context. To tackle these issues, we propose a novel Relation-Aware
Network with Attention-Based Loss (RANA) framework. Specifically, to better
utilize the plentiful negative samples and alleviate the zero-loss issue, we
strategically select relevant negative samples and design an attention-based
loss function to further differentiate the importance of each negative sample.
The intuition is that negative samples more similar to positive samples will
contribute more to the model. Further, we design a dynamic relation-aware
entity encoder for learning a context-dependent entity representation.
Experiments demonstrate that RANA outperforms the state-of-the-art models on
two benchmark datasets.