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.

翻译：少样本知识图谱补全旨在通过给定关系的少样本参考实体对查询未知事实。实体和三元组的不确定性所导致的噪声副作用可能会限制少样本学习，但现有少样本知识图谱补全工作忽略了这种不确定性，使其在噪声存在的有限参考样本面前更为脆弱。本文提出一种新颖的不确定性感知少样本知识图谱补全框架，通过在高斯分布下学习表示来建模不确定性，以更好地理解有限数据。首先设计不确定性表示，在将特征表示转换为高斯分布后估计实体对的不确定性范围。进一步，为更好地整合具有不确定性特征的邻居以增强实体特征，我们设计一种不确定性感知的关系图神经网络，在高斯分布之间执行卷积操作。然后，在高斯分布内对参考三元组进行多次随机采样，以在优化过程中生成平滑的参考表示。每个查询实例的最终补全分数通过所设计的不确定性优化来计算，使我们的方法在少样本场景下对噪声更具鲁棒性。实验结果表明，与同类方法相比，我们的方法在两个基准数据集上取得了优异性能。