修复知识图谱三元组的信息丢失，提升复杂问答准确率
How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback
- 通过恢复三元组原始文本上下文，弥补信息缺失
- 动态引入查询相关的新知识，迭代优化图谱结构
- 适合需要高精度推理的问答系统开发者
知识图谱增强的大语言模型在复杂推理任务中取得显著进展，得益于其广泛领域知识和上下文感知能力。然而，现有方法常假设知识图谱完整，这与实际构建局限及从非结构化文本转为实体-关系三元组时丢失上下文线索的情况不符。为此，本文提出三元组上下文恢复与查询驱动反馈（TCR-QF）框架，通过重建每个三元组背后的原始文本上下文来缓解信息损失，并通过迭代引入查询相关的缺失知识，动态优化知识图谱结构。在五个基准问答数据集上的实验表明，TCR-QF在知识图谱与大语言模型融合中表现优异，相比最先进的GraphRAG方法，在精确匹配（Exact Match）上提升29.1%，在F1分数上提升15.5%。
原文摘要 · Abstract (English)
Knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG construction and the potential loss of contextual cues when converting unstructured text into entity-relation triples. In response, this paper proposes the Triple Context Restoration and Query-driven Feedback (TCR-QF) framework, which reconstructs the textual context underlying each triple to mitigate information loss, while dynamically refining the KG structure by iteratively incorporating query-relevant missing knowledge. Experiments on five benchmark question-answering datasets substantiate the effectiveness of TCR-QF in KG and LLM integration, where itachieves a 29.1% improvement in Exact Match and a 15.5% improvement in F1 over its state-of-the-art GraphRAG competitors.
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