Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classification tasks, like identifying missing triplets, rather than ranking-based tasks, where the model ranks candidate entities based on plausibility. This focus limits the practical use of LLMs in KGC, as real-world applications prioritize highly plausible triplets. Additionally, while graph paths can help infer the existence of missing triplets and improve completion accuracy, they often contain redundant information. To address these issues, we propose KG-CF, a framework tailored for ranking-based KGC tasks. KG-CF leverages LLMs' reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets. The code and datasets are available at \url{https://anonymous.4open.science/r/KG-CF}.

本研究解决了当前知识图谱补全（KGC）研究中的一个重要问题，即如何在排名任务中有效利用大型语言模型。通过提出KG-CF框架，该研究利用模型的推理能力过滤掉不相关的上下文，从而在实际数据集上取得了明显的优越成绩。这一创新方法有望提升知识图谱补全的实用性与准确性。

知识图谱补全与上下文过滤：基于大型语言模型的指导