论文标题
使用RNN划定搜索区域的神经定理掠夺区
Neural Theorem Provers Delineating Search Area Using RNN
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下，它们在知识图中的应用链接预测受到限制。本文提出了一种新的RNNNTP方法，采用了一种基于EM的通用方法来不断提高神经定理掠夺（NTPS）的计算效率。 RNNNTP分为关系发生器和预测因子。关系发生器经过有效和解释的训练，因此可以根据培训的发展进行整个模型，并且计算效率也得到了很大提高。在所有四个数据集中，此方法相对于传统方法以及当前强大的竞争方法之一均显示了链接预测任务的竞争性能。
Although traditional symbolic reasoning methods are highly interpretable, their application in knowledge graphs link prediction has been limited due to their computational inefficiency. A new RNNNTP method is proposed in this paper, using a generalized EM-based approach to continuously improve the computational efficiency of Neural Theorem Provers(NTPs). The RNNNTP is divided into relation generator and predictor. The relation generator is trained effectively and interpretably, so that the whole model can be carried out according to the development of the training, and the computational efficiency is also greatly improved. In all four data-sets, this method shows competitive performance on the link prediction task relative to traditional methods as well as one of the current strong competitive methods.
