Knowledge graph completion (KGC) aims to utilize existing knowledge to deduce
and infer missing connections within knowledge graphs. Text-based approaches,
like SimKGC, have outperformed graph embedding methods, showcasing the promise
of inductive KGC. However, the efficacy of text-based methods hinges on the
quality of entity textual descriptions. In this paper, we identify the key
issue of whether large language models (LLMs) can generate effective text. To
mitigate hallucination in LLM-generated text in this paper, we introduce a
constraint-based prompt that utilizes the entity and its textual description as
contextual constraints to enhance data quality. Our Constrained-Prompt
Knowledge Graph Completion (CP-KGC) method demonstrates effective inference
under low resource computing conditions and surpasses prior results on the
WN18RR and FB15K237 datasets. This showcases the integration of LLMs in KGC
tasks and provides new directions for future research.

本研究使用文本为基础的方法提升了知识图谱补全的效果，并通过引入约束型提示来改善大型语言模型生成的文本质量，该方法在低资源计算条件下展现了有效的推理能力，并在 WN18RR 和 FB15K237 数据集上超越了之前的结果，为将大型语言模型应用于知识图谱补全任务提供了新的研究方向。