Multilingual Knowledge Graph Completion Based on Structure Features of the Dual-Branch

With the development of information fusion,knowledge graph completion tasks have received a lot of attention.some studies investigate the broader underlying problems of linguistics,while embedding learning has a narrow focus.This poses significant challenges due to the heterogeneity of coarse-graining patterns.Then,to settle the whole matter,a framework for completion is designed,named Triple Encoder-Scoring Module(TEsm).The model employs an alternating two-branch structure that fuses local features into the interac-tion pattern of the triplet itself by perfectly combining distance and structure models.Moreover,it is mapped to a uniform shared space.Upon completion,an ensemble inference method is proposed to query multiple predictions from different graphs using a weight classifier.Experiments show that the experimental dataset used for the completion task is DBpedia,which contains five different linguistic subsets..Our extensive experimental results demonstrate that TEsm can efficiently and smoothly solve the optimal completion task,validating the performance of the proposed model.