The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques. This paper introduces a novel methodology, the Knowledge Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP paradigms, including chain-of-thought (CoT) prompting and in-context learning (ICL), to enhance multi-hop link prediction in KGs. By converting the KG to a CoT prompt, our framework is designed to discern and learn the latent representations of entities and their interrelations. To show the efficacy of the KG-LLM Framework, we fine-tune three leading Large Language Models (LLMs) within this framework, employing both non-ICL and ICL tasks for a comprehensive evaluation. Further, we explore the framework's potential to provide LLMs with zero-shot capabilities for handling previously unseen prompts. Our experimental findings discover that integrating ICL and CoT not only augments the performance of our approach but also significantly boosts the models' generalization capacity, thereby ensuring more precise predictions in unfamiliar scenarios.

翻译：知识图谱（KG）中的多链接预测任务是该领域分析中的一个挑战，而自然语言处理（NLP）和知识图谱嵌入技术的进展使其日益可解。本文提出了一种新颖方法——知识图谱大语言模型框架（KG-LLM），该方法利用包括思维链（CoT）提示与上下文学习（ICL）在内的关键NLP范式，以增强知识图谱中的多跳链接预测能力。通过将知识图谱转化为思维链提示，我们的框架旨在识别并学习实体及其相互关系中的潜在表征。为展示KG-LLM框架的有效性，我们在此框架内对三种主流大语言模型（LLMs）进行了微调，并采用非ICL与ICL任务进行综合评估。此外，我们还探索了该框架为LLMs提供零样本能力以处理未见提示的潜力。实验结果发现，整合上下文学习与思维链不仅增强了我们方法的性能，还显著提升了模型的泛化能力，从而确保在陌生场景下实现更精准的预测。