Abstract: In recent years,knowledge graph embedding(KGE) has emerged as a mainstream approach and achieved significant results in the task of knowledge graph completion.However,existing KGE methods only consider the information of triplets at the data level,neglecting the semantic relational patterns that exist between different triplets at the logical level,leading to certain performance deficiencies in current methods.To address this issue,a knowledge graph completion method(RpAT) that integrates relational patterns and analogy transfer is proposed.Firstly,at the logical level,different relational patterns are refined according to the semantic hierarchy of entity relationships.Secondly,at the data level,a method for generating pattern analogy objects is proposed,which utilizes the properties of relational patterns to generate similar analogy objects for target triplets and transfers missing information based on these analogy objects.Finally,a comprehensive scoring function that integrates the reasoning capabilities of the original knowledge graph embedding model and the analogy transfer capabilities is proposed to enhance the performance of graph completion.Experimental results show that,compared to other baseline models,the RpAT method pimproves the MRR values by 15.5% and 1.8% on the FB15k-237 and WN18RR datasets,respectively,demonstrating its effectiveness in the task of knowledge graph completion.

Key words: Knowledge graph, Knowledge graph completion, Relational patterns, Analogy objects, Analogy transfer