结合三元组重要性的知识图谱补全模型

Knowledge Graph Completion Model Based on Triplet Importance Integration

知识图谱是人工智能方向的一个热门研究领域。知识图谱补全是在给定头实体或者尾实体以及相应关系的条件下,补全缺失实体。基于翻译的模型如TransE,TransH和TransR是最常用的一类知识图谱补全方法。然而,大多数现有的补全模型在补全过程中都忽略了知识图谱中三元组重要性的特征。文中提出了一种新型的知识图谱补全模型ImpTransE,该模型考虑了三元组中的重要性特征,设计了实体重要性排序方法KGNodeRank和多粒度关系重要性估计方法MG-RIE,分别对实体重要性和关系重要性进行估计。具体来说,KGNodeRank通过同时考虑关联结点的重要性及其重要性传递方向的概率来估计实体结点的重要性排名。MG-RIE则同时考虑了关系的一阶重要性和高阶重要性,从而对关系的总体重要性进行合理估计。ImpTransE同时考虑了三元组的实体重要性和关系重要性特征,使其在学习过程中对于不同的三元组信息可赋予不同的关注程度,提高了模型的表示学习性能,从而达到了更好的补全效果。实验结果表明,在两类知识图谱数据集中与5种对比模型相比,ImpTransE模型在大部分指标上均具有最佳的补全性能,对不同数据集的补全效果获得了一致的提升。

Knowledge graph is a popular research area related to artificial intelligence.Knowledge graph completion is the completion of missing entities given head or tail entities and corresponding relations.Translation models(such as TransE,TransH and TransR)are one of the most commonly used completion methods.However,most of the existing completion models ignore the feature of the importance of the triplets in the knowledge graph during the completion process.This paper proposes a novel knowledge graph completion model,ImpTransE,which takes into account the importance feature in triplets,and designs the entity importance ranking method KGNodeRank and the multi-grained relation importance estimation method MG-RIE,to estimate the entity importance and relation importance,respectively.Specifically,the KGNodeRank method estimates the entity node importance ranking by considering both the importance of the associated nodes and the probability that their importance is transmitted,while the MG-RIE method considers multi-order relation importance to provide a reasonable estimate of the overall importance of the relation.ImpTransE takes into account the entity importance and relation importance features of triplets,so that different le-vels of attention are given to different triplets during the learning process,which improves the learning performance of the ImpTransE model and thus achieves better completion performance.Experimental results show that ImpTransE model has the best completion performance in most of the metrics on the two knowledge graph datasets compared with the five comparison models,and completion performance of different datasets is consistently improved.