  Knowledge graphs (KGs), as a structured form of knowledge representation,
have been widely applied in the real world. Recently, few-shot knowledge graph
completion (FKGC), which aims to predict missing facts for unseen relations
with few-shot associated facts, has attracted increasing attention from
practitioners and researchers. However, existing FKGC methods are based on
metric learning or meta-learning, which often suffer from the
out-of-distribution and overfitting problems. Meanwhile, they are incompetent
at estimating uncertainties in predictions, which is critically important as
model predictions could be very unreliable in few-shot settings. Furthermore,
most of them cannot handle complex relations and ignore path information in
KGs, which largely limits their performance. In this paper, we propose a
normalizing flow-based neural process for few-shot knowledge graph completion
(NP-FKGC). Specifically, we unify normalizing flows and neural processes to
model a complex distribution of KG completion functions. This offers a novel
way to predict facts for few-shot relations while estimating the uncertainty.
Then, we propose a stochastic ManifoldE decoder to incorporate the neural
process and handle complex relations in few-shot settings. To further improve
performance, we introduce an attentive relation path-based graph neural network
to capture path information in KGs. Extensive experiments on three public
datasets demonstrate that our method significantly outperforms the existing
FKGC methods and achieves state-of-the-art performance. Code is available at
https://github.com/RManLuo/NP-FKGC.git.
