Abstract: A knowledge graph embedding representation method and a related device are disclosed. The method includes: obtaining, from a preset knowledge base, N related entities of each entity in M entities of a target knowledge graph and K concepts corresponding to each of the N related entities, determining a semantic correlation between each entity and each of the N related entities of the entity, determining a first entity embedding representation of each of the N related entities based on the corresponding K concepts, modeling, based on the first entity embedding representation and the semantic correlation, an entity/relationship embedding representation, and training a model according to an attention mechanism and a preset model training method, to obtain the entity/relationship embedding representation.