Abstract: Knowledge graph completion aims to infer new facts based on existing facts,enhance the comprehensiveness and reliability of the knowledge graph,and thus improve its practical value.In order to solve the problems that existing methods based on pre-trained language models have large differences in the prediction effects of head and tail entities,large fluctuations in the training process due to the stochastic initialization of consecutive prompts,and under-utilization of structural information of the know-ledge graph,this paper proposes the knowledge graph completion model using semantically enhanced prompts and structural information(SEPS-KGC).The model follows a multi-task learning framework that unites the knowledge graph completion task with the entity prediction task.Firstly,an example-guided relationship templates generation method is designed to generate two more targeted relationship prompt templates using a large language model for the different tasks of predicting head entities and predicting tail entities,and incorporating semantic auxiliary information to enable the model to better understand the semantic associations between entities.Secondly,a prompt learning method based on effective initialization is designed,using pre-trained embeddings of relational labels for initialization.Finally,a structural information extraction module is designed to extract knowledge graph structural information using convolution and pooling operations to improve the stability and relationship understanding of the model.The effectiveness of SEPS-KGC is demonstrated on two public datasets.

Key words: Knowledge graph, Knowledge graph completion, Pre-trained language model, Large language model, Prompt learning, Structural information