RNNCTPs：一种基于动态知识划分的神经元符号推理方法
RNNCTPs: A Neural Symbolic Reasoning Method Using Dynamic Knowledge Partitioning Technology
摘要：
传统的符号推理方法虽然具有高度的可解释性，但由于计算效率低，在知识图谱链接预测中的应用受到限制。在本文中，我们提出了一种新的神经符号推理方法：RNNCTPs，它通过重新过滤条件定理证明器（CTPs）的知识选择，提高了计算效率，并且对嵌入大小参数不太敏感。RNNCTP分为关系选择器和预测器。关系选择器被有效地和可解释地训练，使得整个模型可以动态地生成用于预测器的推理的知识。在所有四个数据集上，该方法在链接预测任务上均表现出与传统方法相比较的竞争性能，并且相对于CTP，在数据集的选择上具有更高的适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
