A semantic guide-based embedding method for knowledge graph completion

Knowledge graph embedding aims to map entities and relations into a low-dimensional vector space for easy manipulation. However, frequent entities are updated more often than infrequent ones during training, leading to inadequate representation of the latter's embeddings, which, in turn, affects the model's overall performance in downstream tasks. To address this issue, we propose a semantic information guide and enhance (SGE) method. The SGE tackles the heterogeneity in the frequency of entities through semantic reconstruction and a guidance network. The semantic reconstruction strengthens the semantic relevance among all entities and connects entities with different frequencies in semantic space. The guidance network extends these connections to knowledge space, enhancing the expression abilities of infrequent entities' embeddings without compromising the embeddings of frequent entities. Experiments with four commonly used benchmark datasets show that the SGE method improves the performance of baseline models in most cases and that the method is model-independent.