Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.

翻译：大部分传统知识嵌入方法将两个实体(概念和实例)和关系作为矢量纳入一个低维的语义空间,同时忽略概念和实例之间的差异。在本文中,我们提出一个新的知识图,嵌入名为TransC的模型,通过区分概念和实例。具体地说,TransC将每个概念纳入知识图中,将每个概念作为一个球体,并将每个实例作为同一语义空间的矢量。我们使用相对位置来模拟概念和实例(例如,实例)之间的关系以及概念和子概念之间的关系(例如,子分类)。我们评估了我们关于基于YAGO的数据集的连接预测和三重分类任务的模式。实验结果表明,TransC超越了最新技术方法,并记录了实例和子分类关系中的语义过渡性。我们的代码和数据集可以从 https:// github.com/davidlvin/transinmations