Dynamic Knowledge Graph Inference Based on Multiple Relational Cyclic Events

Abstract

The reasoning ability of most existing dynamic knowledge map reasoning methods under the same time and multiple relationships is limited.Aiming at this problem,a method of dynamic knowledge graph inference based on multi-relational cyclic events(Multi-Net)is proposed.The improved multi-relational proximity aggregator is employed to fuse target entity neighborhood information to obtain more accurate representation of entity neighborhood vector,and Multi-Net is simplified by optimizing information fusion,and the ability to handle the conflict of relations between two entities in a specific scope is improved by adding the relationship prediction task to Multi-Net.Experiments of entity prediction and relationship prediction on large real datasets indicate that Multi-Net improves the reasoning ability of dynamic knowledge maps effectively.