融合多跳关系路径信息的关系推理方法

针对目前知识图谱（KG）中存在大量关系的缺失，以及在进行关系推理时没有充分考虑两实体间多跳路径中隐含信息的问题，提出了一种融合多跳关系路径信息的关系推理方法。首先，对于给定的候选关系和两个实体，利用卷积运算将连接两个实体的多跳关系路径编码到低维空间里并提取信息；其次，利用双向长短时记忆（BiLSTM）网络建模以生成关系路径表示向量，并利用注意力机制将其与候选关系表示向量进行组合；最后，采用多步推理方式找到匹配程度最高的关系作为推理结果并判断其精确率。与目前常用的路径排序算法（PRA）、神经网络模型Path-RNN以及强化学习模型MINERVA相比，在使用大型知识图谱数据集NELL995进行实验时，所提算法的平均精确率均值（MAP）分别提高了1.96、8.6和1.6个百分点；在使用小型知识图谱数据集Kinship进行实验时，所提方法的MAP比PRA、MINERVA分别提高了21.3、13和12.1个百分点。实验结果表明，所提算法能更加准确地推理出实体间的关系链接。

Relationship reasoning method combining multi-hop relationship path information

Concerning the problems of the lack of a large number of relationships in the current Knowledge Graph (KG), and the lack of full consideration of the hidden information in the multi-hop path between two entities when performing relationship reasoning, a relationship reasoning method combining multi-hop relationship path information was proposed. Firstly, for the given candidate relationships and two entities, the convolution operation was used to encode the multi-hop relationship path connecting the two entities into a low-dimensional space and extract the information. Secondly, the Bidirectional Long Short-Term Memory (BiLSTM) network was used for modeling to generate the relationship path representation vector, and the attention mechanism was used to combine it with the candidate relationship representation vector. Finally, a multi-step reasoning method was used to find the relationship with the highest matching degree as the reasoning result and judge its precision. Compared with the current popular Path Ranking Algorithm (PRA), the neural network model named Path-RNN and reinforcement learning model named MINERVA, the proposed algorithm had the Mean Average Precision (MAP) increased by 1.96,8.6 and 1.6 percentage points respectively when using the large-scale knowledge graph dataset NELL995 for experiments. And when using the small-scale knowledge graph dataset Kinship for experiments, the proposed algorithm had the MAP improved by 21.3,13 and 12.1 percentage points respectively compared to PRA and MINERVA. The experimental results show that the proposed method can infer the relationship links between entities more accurately.