  Knowledge graph (KG) reasoning is a task that aims to predict unknown facts
based on known factual samples. Reasoning methods can be divided into two
categories: rule-based methods and KG-embedding based methods. The former
possesses precise reasoning capabilities but finds it challenging to reason
efficiently over large-scale knowledge graphs. While gaining the ability to
reason over large-scale knowledge graphs, the latter sacrifices reasoning
accuracy. This paper aims to design a reasoning framework called Neural
Probabilistic Logic Learning(NPLL) that achieves accurate reasoning on
knowledge graphs. Our approach introduces a scoring module that effectively
enhances the expressive power of embedding networks, striking a balance between
model simplicity and reasoning capabilities. We improve the interpretability of
the model by incorporating a Markov Logic Network based on variational
inference. We empirically evaluate our approach on several benchmark datasets,
and the experimental results validate that our method substantially enhances
the accuracy and quality of the reasoning results.
