用大模型增强知识图谱，让问答更准更省力
Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching
- 用大模型补全知识图谱，缩小查询与图谱的语义差距
- 在两个基准数据集上达到顶尖性能，且计算开销低
- 适合做知识密集型推理的开发者和研究者参考
大型语言模型在复杂任务中表现出强大的推理能力，但在知识图谱问答等知识密集型场景下仍存在幻觉和事实错误。我们将其归因于结构化知识图谱与非结构化查询之间的语义鸿沟，源于两者关注点和结构的固有差异。现有方法通常采用资源消耗大、难以扩展的流程直接在原始知识图谱上推理，却忽略了这一关键问题。为此，我们提出灵活框架Enrich-on-Graph（EoG），利用大模型的先验知识对知识图谱进行增强，弥合查询与图谱间的语义差距。EoG实现高效证据抽取，支持精准稳健推理，同时保持低计算成本、可扩展性及对多种方法的适应性。此外，我们设计了三项图谱质量评估指标，用于分析知识图谱问答中的查询-图谱对齐情况，并提供了优化目标的理论验证。在两个知识图谱问答基准数据集上的大量实验表明，EoG能有效生成高质量知识图谱并取得当前最优性能。代码与数据已开源。
原文摘要 · Abstract (English)
Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA). We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures. Existing methods usually employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap. To address this challenge, we propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries. EoG enables efficient evidence extraction from KGs for precise and robust reasoning, while ensuring low computational costs, scalability, and adaptability across different methods. Furthermore, we propose three graph quality evaluation metrics to analyze query-graph alignment in KGQA task, supported by theoretical validation of our optimization objectives. Extensive experiments on two KGQA benchmark datasets indicate that EoG can effectively generate high-quality KGs and achieve the state-of-the-art performance. Our code and data are available at https://github.com/zjukg/Enrich-on-Graph.
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