Multi-relational knowledge graph completion method with local information fusion

Knowledge graph completion(KGC) has attracted increasing attention in recent years, aiming at complementing missing relationships between entities in a Knowledge Graph(KG). While the existing KGC approaches utilizing the knowledge within KG could only complement a very limited number of missing relations, more and more approaches tend to study the completion of the multi-relationship knowledge graph. However, the existing completion methods of multi-relation knowledge graph regard knowledge graph as an undirected graph, which ignores the directionality of knowledge graph, so that the potential characteristics of multi-relation cannot be learned. Besides, most algorithms fail to explore the local information of knowledge because they ignore the different importance of entity adjacencies. In this paper, we propose to use local information fusion to join the entity and its adjacency relation, to acquiring the multi-relation representation. In addition, we try to specify distinct weights to model the direction of the relationship and apply the attention mechanism between entity nodes to obtain local information between entity nodes. Experiments conducted on three benchmark datasets and a medical domain knowledge graph dataset that we collect demonstrate the effectiveness of the proposed framework.