论文标题
RNNCTP：一种使用动态知识分配技术的神经符号推理方法
RNNCTPs: A Neural Symbolic Reasoning Method Using Dynamic Knowledge Partitioning Technology
论文作者
论文摘要
尽管传统的符号推理方法是高度可解释的，但由于其计算效率低，它们在知识图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：RNNCTP，它通过重新过滤条件定理掠夺者（CTPS）的知识选择来提高计算效率，并且对嵌入尺寸参数的敏感性较小。 RNNCTP分为关系选择器和预测因子。关系选择器经过有效和解释的训练，以便整个模型可以动态地生成预测因子的推理知识。在所有四个数据集中，该方法均针对链接预测任务的传统方法均显示竞争性能，并且相对于CTP的数据集选择更高的适用性。
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.
