Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC. They can be categorized into two main classes: triple-based and text-based approaches. Triple-based methods struggle with long-tail entities due to limited structural information and imbalanced entity distributions. Text-based methods alleviate this issue but require costly training for language models and specific finetuning for knowledge graphs, which limits their efficiency. To alleviate these limitations, in this paper, we propose KICGPT, a framework that integrates a large language model (LLM) and a triple-based KGC retriever. It alleviates the long-tail problem without incurring additional training overhead. KICGPT uses an in-context learning strategy called Knowledge Prompt, which encodes structural knowledge into demonstrations to guide the LLM. Empirical results on benchmark datasets demonstrate the effectiveness of KICGPT with smaller training overhead and no finetuning.

翻译：知识图谱补全（KGC）对于解决知识图谱不完整性问题、支持下游应用至关重要。已有许多模型被提出用于KGC，可分为两大类：基于三元组的方法和基于文本的方法。基于三元组的方法由于结构信息有限和实体分布不均衡，在处理长尾实体时面临困难。基于文本的方法虽缓解了该问题，但需对语言模型进行昂贵训练并针对知识图谱进行特定微调，限制了其效率。为克服这些局限，本文提出KICGPT框架，该框架集成大语言模型（LLM）与基于三元组的KGC检索器，可在无需额外训练开销的情况下缓解长尾问题。KICGPT采用名为"知识提示"（Knowledge Prompt）的上下文学习策略，通过将结构知识编码至示例中引导LLM。基准数据集上的实验结果表明，KICGPT在训练开销更小且无需微调的情况下具有有效性。