Differentiating Concepts and Instances for Knowledge Graph Embedding
Xin Lv, Lei Jing Hou, Juanzi Li, Zhiyuan Liu · 2018
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.