Abstract: Knowledge graph embedding provides a more powerful knowledge representation input to machine learning models by projecting entities and relationships into a continuous low-dimensional vector space,thereby supporting more knowledge graph application scenarios.In recent years,researchers have tried to use the potential semantic information between ontology and instance in knowledge graph to enhance the embedding of knowledge graph.However,they fail to effectively integrate the hierarchical structure of concepts and the specific information of instances,and ignore the transitivity between isA relationships,resulting in limited performance and generalization ability of the models when dealing with long-tail entities in the knowledge graph.In order to solve the above shortcomings,this paper proposes a knowledge graph embedding model(Representation Learning of Knowledge Graph via Jointly Modeling Ontology and Instances,JMOI),which integrates ontology and instance.By introducing self-attention mechanism,this model captures the complex semantic relationship between concepts and instances,and adds a learnable parameter to adjust the neighborhood range of concept embedding,so as to distinguish the hierarchical information of diffe-rent concepts.The transitivity of isA relationship is modeled.Experimental results on the YAGO26K-906 and DB111K-174 datasets show that JMOI achieves the best performance in most cases compared with the prior art,with a maximum improvement of 6.5% in the link prediction Hits@1 and 6.9% in the Recall in triple classification compared with the suboptimal model.

Key words: Knowledge graph, Knowledge graph embedding, Concepts and instances, Link prediction, Triple classification