RNNCTPs: A neural symbolic reasoning method using dynamic knowledge partitioning technology

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 is 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 predictor. In all four datasets, this method shows competitive performance against traditional methods on the link prediction task, and can have higher applicability than CTPs on some datasets.