Abstract: In the field of knowledge graph completion,the rich multi-semantic information between entities and relationships is of great significance for improving the accuracy of completion tasks.However,existing models often struggle to fully capture and integrate these multi-semantic features,which limits the effectiveness of completion.To address this challenge,this paper proposes a Knowledge Graph Completion Model Based on Multi-Semantic Extraction(MSE).Firstly,a multi-semantic aggregation encoder is designed to dimensionally split entity and relationship embeddings,integrating the multi-semantic information of neighboring entities and relationships.Secondly,a decoder based on multi-scale convolution is proposed,using convolutional kernels of different sizes to extract the deep semantic features of entities.Lastly,a loss function with independence constraints is designed,introducing a regularization term based on Pearson correlation coefficients to enhance the model’s multi-semantic expression capability.The experimental results show that on the FB15k-237 and WN18RR datasets,the MRR values of the MSE model are improved by 1.7% and 2.3%,respectively,compared with the optimal models of other baselines,which verifies its effectiveness on the knowledge graph complementation task.

Key words: Knowledge graph, Knowledge completion, Multi-semantic information, Independence constraint