融合实体描述及类型的知识图谱表示学习方法

Representation Learning of Knowledge Graph Integrating Entity Description and Entity Type

知识图谱在很多人工智能领域发挥着越来越重要的作用。知识图谱表示学习旨在将三元组中的实体和关系映射到低维稠密的向量空间。TransE、TransH和TransR等基于翻译操作的表示学习方法,只考虑了知识图谱的三元组信息孤立的学习表示,未能有效利用实体描述、实体类型等重要信息,从而不能很好地处理一对多、多对多等复杂关系。针对这些问题,该文提出了一种融合实体描述及类型的知识图谱表示学习方法。首先,利用Doc2Vec模型得到全部实体描述信息的嵌入;其次,对实体的层次类型信息进行表示,得到类型的映射矩阵,结合Trans模型的三元组嵌入,得到实体类型信息的表示;最后,对三元组嵌入、实体描述嵌入及实体类型嵌入进行连接操作,得到最终实体嵌入的表示,通过优化损失函数训练模型,在真实数据集上分别通过链接预测和三元组分类两个评测任务进行效果评估,实验结果表明新方法优于TransE、TransR、DKRL、SimplE等主流模型。

Representation learning of knowledge graph aims to project entities and relations into continuous low-dimensional vector space.Most existing translation-based representation methods,such as TransE、TransH and TransR,usually utilize only triples of knowledge graph,and fail to deal with complex relationships such as one-to-many,many-to-one,and many-to-many.To address this issue,this paper proposes a representation learning model of knowledge graph integrating entity description and type,which is called TDT model.Firstly,the Doc2 Vec model is used to obtain the embedding of all entity descriptions.Secondly,treating the hierarchical types as projection matrices for entities,the embedding of entity type information can be obtained via multiplying the projection matrix with triple embedding.Finally,TDT model integrates the information of triple(T),entity description(D),and entity type information(T)in a low-dimensional vector space.This paper evaluates TDT model via the experiments of link prediction and triple classification on the real-world datasets.The results show that new method significantly outperforms other baselines,such as TransE,TransR,DKRL and SimplE etc.