基于多个关系循环事件的动态知识图推理

Q4 Computer Science
陈浩, 李永强, 冯远静
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引用次数: 0

摘要

现有的大多数动态知识地图推理方法在同一时间和多个关系下的推理能力有限。针对这一问题,提出了一种基于多关系循环事件(Multi-Net)的动态知识图推理方法。采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,通过优化信息融合简化Multi-Net,并在Multi-Net中增加关系预测任务,提高处理特定范围内两个实体之间关系冲突的能力。在大型真实数据集上进行的实体预测和关系预测实验表明,Multi-Net有效地提高了动态知识地图的推理能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
模式识别与人工智能
模式识别与人工智能 Computer Science-Artificial Intelligence
CiteScore
1.60
自引率
0.00%
发文量
3316
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