Tucker inference learning method for knowledge graph decision making

This study aims to build a framework called Tucker Reasoning Learning Method to train the upper layer knowledge graph (KG) making explainable and reasonable decisions.The numerical experiments show that the accuracy is 84%.The contributions are as follows: (i) It overcomes in-explainable problems of using deep learning method; (ii) It has more feedback rings and reasonable paths than decision tree method; (iii) Compared with RESCAL's application in reasoning domain, it enhances 22 percentage points.It is suitable for application scenarios like inancial, justice, and medical decision-making, which require explainable and reasoning paths.This study builds a framework called Tucker Reasoning Learning Method to train the upper layer knowledge graph to make explainable and reasonable decisions.The method has the accuracy of 84%, which enhances 22 percentage points compared to the SOTA methods.