摘要
针对目前知识图谱中存在关系事实缺失且对隐含知识挖掘不足等问题,提出一种基于多级关系路径语义组合的关系推理算法。将知识图谱嵌入到低维向量空间中,利用强化学习进行路径发现,使得路径中实体和关系对应的向量作为循环神经网络的输入,经过迭代学习输出多级关系路径语义组合的结果向量,并将结果向量与目标关系向量进行相似度计算,从而进行关系推理。在FB15K-237和NELL-995数据集上的实验结果表明,该算法事实预测精度分别为0.314和0.417,均优于PRA、TransE与TransH模型。
To address the lack of relationship facts and insufficient mining of hidden knowledge in the current Knowledge Graph(KG),this paper proposes a relationship reasoning algorithm based on the semantic combination of multi-hop relational path.The algorithm embeds KG into a low-dimensional vector space and uses reinforcement learning for path discovery,so that the vectors corresponding to the entities and relationships in the path are used as the input of the Recurrent Neural Network(RNN).After iterative learning,the result vector of the semantic combination of multi-level relation path is output,and the similarity between the result vector and the target relation vector is calculated.Experimental results on the FB15K-237 and NELL-995 datasets show that the factual prediction results of the proposed algorithm are 0.314 and 0.417 respectively,which are better than the PRA,TransE,and TransH models.
作者
陈恒
韩雨婷
李冠宇
王京徽
CHEN Heng;HAN Yuting;LI Guanyu;WANG Jinghui(School of Information Science and Technology,Dalian Maritime University,Dalian,Liaoning 116026,China;School of Software,Dalian University of Foreign Languages,Dalian,Liaoning 116044,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第5期109-114,共6页
Computer Engineering
基金
国家自然科学基金(61371090)
辽宁省自然科学基金重点项目(20170540232)
辽宁省重点研发计划指导项目(61801007)
大连外国语大学科研基金(2018CXTD04)。
关键词
关系推理
表示学习
强化学习
循环神经网络
知识图谱
语义组合
relational reasoning
represent learning
reinforcement learning
Recurrent Neural Network(RNN)
Knowledge Graph(KG)
semantic combination
