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.

翻译：虽然传统的象征性推理方法极易解释,但由于计算效率低,它们在知识图形链接预测中的应用有限。在本文中,我们提议一种新的神经符号推理方法:RNNCTPs,它通过重新过滤有条件理论预测(CTPs)的知识选择来提高计算效率,对嵌入大小参数不那么敏感。RNNCTPs分为关系选择器和预测器。关系选择器经过高效和可解释的培训,因此整个模型能够动态地生成预测器的推断知识。在所有四个数据集中,该方法显示相对于链接预测任务的传统方法的竞争性性能,对于选择与 CTPs有关的数据集具有更高的适用性。