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The design and development of text-based knowledge graph completion (KGC)methods leveraging textual entity descriptions are at the forefront ofresearch. These methods involve advanced optimization techniques such as softprompts and contrastive learning to enhance KGC models. The effectiveness oftext-based methods largely hinges on the quality and richness of the trainingdata. Large language models (LLMs) can utilize straightforward prompts to altertext data, thereby enabling data augmentation for KGC. Nevertheless, LLMstypically demand substantial computational resources. To address these issues,we introduce a framework termed constrained prompts for KGC (CP-KGC). ThisCP-KGC framework designs prompts that adapt to different datasets to enhancesemantic richness. Additionally, CP-KGC employs a context constraint strategyto effectively identify polysemous entities within KGC datasets. Throughextensive experimentation, we have verified the effectiveness of thisframework. Even after quantization, the LLM (Qwen-7B-Chat-int4) still enhancesthe performance of text-based KGC methods \footnote{Code and datasets areavailable at\href{https://github.com/sjlmg/CP-KGC}{https://github.com/sjlmg/CP-KGC}}. Thisstudy extends the performance limits of existing models and promotes furtherintegration of KGC with LLMs.

Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic Enhancement