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.

提出了一种整合了大型语言模型和三元组为基础的知识图谱完成方法，名为 KICGPT，通过使用知识提示来解决长尾问题并改进效率，而无需额外的训练和微调。