基于多关系循环事件的动态知识图谱推理

针对现有动态知识图谱推理方法大多存在同时间多关系下推理能力有限的问题,文中提出基于多关系循环事件的动态知识图谱推理方法.利用改进的多关系邻近聚合器融合目标实体邻域信息,获得更准确的实体邻域向量表示,并通过优化信息融合简化文中方法.同时加入关系预测任务,扩大处理特定范围内两个实体间关系冲突的能力.在大型真实数据集上的实体预测和关系预测的实验表明,文中方法有效提升动态知识图谱的推理能力.

Dynamic Knowledge Graph Inference Based on Multiple Relational Cyclic Events

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.