  Although traditional symbolic reasoning methods are highly interpretable,
their application in knowledge graph link prediction is limited due to their
low computational efficiency. In this paper, we propose a new neural symbolic
reasoning method: RNNCTPs, which improves computational efficiency by
re-filtering the knowledge selection of Conditional Theorem Provers (CTPs), and
is less sensitive to the embedding size parameter. RNNCTPs are divided into
relation selectors and predictors. The relation selectors are trained
efficiently and interpretably, so that the whole model can dynamically generate
knowledge for the inference of the predictor. In all four datasets, the method
shows competitive performance against traditional methods on the link
prediction task, and can have higher applicability to the selection of datasets
relative to CTPs.
