TransRD: 一种不对等特征的知识图谱嵌入表示模型

知识图谱嵌入是一种将实体和关系映射到低维向量空间的技术。目前已有的嵌入表示方法在对具有不对等特征的知识图谱中的实体和关系建模时存在两大缺陷: 一是假定头尾实体来自同一语义空间,忽略二者在链接结构和数量上的不对等;二是每个关系单独配置一个投影矩阵,忽略关系之间的内在联系,导致知识共享困难,泛化能力差。该文提出一种新的嵌入表示方法TransRD,首先对头尾实体采用不对等转换矩阵进行投影,并用ADADELTA算法自适应调整学习率;其次对关系按相关性分组,每组关系使用同一对投影矩阵的方式来共享公共信息,解决泛化能力差的问题。在公开的数据集WN18和FB15K以及MPBC_20(乳腺癌知识图谱的子集)上进行实验和结果分析并与现有的模型进行对比,结果表明TransRD在各项指标上均取得大幅提升。

TransRD: Embedding of Knowledge Graph with Asymmetric Features

Knowledge graph embedding maps entities and relations into low-dimensional vector spaces. Existing embedding representation methods have two major drawbacks in modeling knowledge graph with asymmetric characteristics. First, they do not consider asymmetry between head and tail entities, assuming that the head and tail entities in knowledge graphs come from the same semantic spaces. Second, they equip each relation with a set of unique projection matrices, ignoring the intrinsic correlations of relations, which hinder the sharing of knowledge between projection matrices and cause poor generalization ability. This paper proposes a novel embedding approach named Trans-RD to deal with the two issues above. TransRD adopts different projection matrices for head and tail entities respectively, and applies ADADELTA algorithm to adjust the learning rate adaptively. Then it uses the same pair of transfer matrices for similar relations to improve the performance of knowledge representation. Empirical results of link prediction based on WN18 and FB15K (public knowledge graph datasets) and MPBC_20 (a subset of Knowledge Graph of Breast Cancer) show that TransRD achieves remarkable improvement in various aspects compared to existing models.