Knowledge Graph Embedding via Dynamic Mapping Matrix
Knowledge Graph Embedding via Dynamic Mapping Matrix
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DOI：
10.3115/v1/p15-1067
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Guoliang Ji;Shizhu He;Liheng Xu;Kang Liu;Jun Zhao
中科院分区：
文献类型：
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作者：
Guoliang Ji;Shizhu He;Liheng Xu;Kang Liu;Jun Zhao
知识图是许多人工智能应用程序的有用资源，但它们远未完成。以前的工作，如transE，transH和transR/CtransR的关系作为从头实体到尾实体的翻译和CtransR实现了最先进的性能。在本文中，我们提出了一个更细粒度的模型名为transD，这是一个改进的transR/CtransR。在TransD中，我们使用两个向量来表示命名符号对象（实体和关系）。第一个表示一个（n）实体（关系）的意义，另一个用于动态地构造映射矩阵。与TransR/CTransR相比，TransD不仅考虑了关系的多样性，而且还考虑了实体的多样性。TransD算法参数少，不需要矩阵向量乘法运算，适用于大规模图。在实验中，我们评估了我们的模型在两个典型的任务，包括三元组分类和链接预测。评估结果表明，我们的方法优于国家的最先进的方法。
Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper, we propose a more fine-grained model named TransD, which is an improvement of TransR/CTransR. In TransD, we use two vectors to represent a named symbol object (entity and relation). The first one represents the meaning of a(n) entity (relation), the other one is used to construct mapping matrix dynamically. Compared with TransR/CTransR, TransD not only considers the diversity of relations, but also entities. TransD has less parameters and has no matrix-vector multiplication operations, which makes it can be applied on large scale graphs. In Experiments, we evaluate our model on two typical tasks including triplets classification and link prediction. Evaluation results show that our approach outperforms state-of-the-art methods.