  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.
