  The design and development of text-based knowledge graph completion (KGC)
methods leveraging textual entity descriptions are at the forefront of
research. These methods involve advanced optimization techniques such as soft
prompts and contrastive learning to enhance KGC models. The effectiveness of
text-based methods largely hinges on the quality and richness of the training
data. Large language models (LLMs) can utilize straightforward prompts to alter
text data, thereby enabling data augmentation for KGC. Nevertheless, LLMs
typically demand substantial computational resources. To address these issues,
we introduce a framework termed constrained prompts for KGC (CP-KGC). This
CP-KGC framework designs prompts that adapt to different datasets to enhance
semantic richness. Additionally, CP-KGC employs a context constraint strategy
to effectively identify polysemous entities within KGC datasets. Through
extensive experimentation, we have verified the effectiveness of this
framework. Even after quantization, the LLM (Qwen-7B-Chat-int4) still enhances
the performance of text-based KGC methods \footnote{Code and datasets are
available at
\href{https://github.com/sjlmg/CP-KGC}{https://github.com/sjlmg/CP-KGC}}. This
study extends the performance limits of existing models and promotes further
integration of KGC with LLMs.
