Embedding Entity and Relation for Knowledge Graph by Probability Directed Graph

Chunming Yang, Xinghao Song, Yue Luo, Litao Zhang

Abstract: Knowledge graph embedding (KGE) represents entities and relationship as low dimensional dense vectors in knowledge graphs (KGs), and to improve the computational efficiency of downstream tasks. This paper regards the semantic relations between the head and tail entities in the KGs as semantically probability transfers, and proposes a method based on the structured probability model for KGE. This method considers the relations as nodes and uses probability-directed graphs to model the KGs. The scoring function is defined as a probability distribution that represents the directed transitivity between the entities and relations. Finally, the function is used to infer the probability that the triples are true. Experimental results on several standard datasets show that this method achieves better results in complex relations and uneven distribution of triples.