基于多级关系路径语义组合的关系推理算法

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。

Relational Reasoning Algorithm Based on Semantic Combination of Multi-hop Relational Path

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.