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Knowledge graph completion (KGC) revolves around populating missing triplesin a knowledge graph using available information. Text-based methods, whichdepend on textual descriptions of triples, often encounter difficulties whenthese descriptions lack sufficient information for accurate prediction-an issueinherent to the datasets and not easily resolved through modeling alone. Toaddress this and ensure data consistency, we first use large language models(LLMs) to generate coherent descriptions, bridging the semantic gap betweenqueries and answers. Secondly, we utilize inverse relations to create asymmetric graph, thereby providing augmented training samples for KGC.Additionally, we employ the label information inherent in knowledge graphs(KGs) to enhance the existing contrastive framework, making it fullysupervised. These efforts have led to significant performance improvements onthe WN18RR and FB15k-237 datasets. According to standard evaluation metrics,our approach achieves a 4.2% improvement in Hit@1 on WN18RR and a 3.4%improvement in Hit@3 on FB15k-237, demonstrating superior performance.

KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation