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.

翻译：知识图谱补全旨在利用现有知识推导并推断知识图谱中缺失的连接。基于文本的方法（如SimKGC）已超越图嵌入方法，展现了归纳式知识图谱补全的潜力。然而，文本方法的有效性依赖于实体文本描述的质量。本文识别出关键问题：大语言模型能否生成有效文本。为缓解大语言模型生成文本中的幻觉现象，我们提出一种基于约束的提示方法，利用实体及其文本描述作为上下文约束以提升数据质量。所提出的CP-KGC方法在低资源计算条件下实现了高效推理，并在WN18RR和FB15K237数据集上超越了先前结果。这展示了大语言模型在知识图谱补全任务中的整合应用，并为未来研究提供了新方向。