  Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of
a relation given its few-shot reference entity pairs. The side effect of noises
due to the uncertainty of entities and triples may limit the few-shot learning,
but existing FKGC works neglect such uncertainty, which leads them more
susceptible to limited reference samples with noises. In this paper, we propose
a novel uncertainty-aware few-shot KG completion framework (UFKGC) to model
uncertainty for a better understanding of the limited data by learning
representations under Gaussian distribution. Uncertainty representation is
first designed for estimating the uncertainty scope of the entity pairs after
transferring feature representations into a Gaussian distribution. Further, to
better integrate the neighbors with uncertainty characteristics for entity
features, we design an uncertainty-aware relational graph neural network
(UR-GNN) to conduct convolution operations between the Gaussian distributions.
Then, multiple random samplings are conducted for reference triples within the
Gaussian distribution to generate smooth reference representations during the
optimization. The final completion score for each query instance is measured by
the designed uncertainty optimization to make our approach more robust to the
noises in few-shot scenarios. Experimental results show that our approach
achieves excellent performance on two benchmark datasets compared to its
competitors.
