通过问答引导重评分与注入，提升知识图谱问答的准确性
Question-guided Knowledge Graph Re-scoring and Injection for Knowledge Graph Question Answering
- 用问题引导重评分剔除无关知识路径
- 在多个基准上优于现有系统，显著提升问答准确率
- 适合需要精准事实推理的大模型应用
知识图谱问答（KGQA）通过利用存储在知识图谱中的结构化信息来回答自然语言问题。通常，KGQA首先从大规模知识图谱中检索出目标子图，作为推理模型处理查询的基础。然而，检索到的子图不可避免地包含干扰信息，影响模型进行准确推理的能力。为此，我们提出一种问答引导的知识图谱重评分方法（Q-KGR），以消除输入问题相关的噪声路径，从而聚焦于相关事实知识。此外，我们引入Knowformer，一种参数高效的将重评分后的知识图谱注入大型语言模型的方法，以增强其事实推理能力。在多个KGQA基准上的大量实验表明，该方法优于现有系统。
原文摘要 · Abstract (English)
Knowledge graph question answering (KGQA) involves answering natural language questions by leveraging structured information stored in a knowledge graph. Typically, KGQA initially retrieve a targeted subgraph from a large-scale knowledge graph, which serves as the basis for reasoning models to address queries. However, the retrieved subgraph inevitably brings distraction information for knowledge utilization, impeding the model's ability to perform accurate reasoning. To address this issue, we propose a Question-guided Knowledge Graph Re-scoring method (Q-KGR) to eliminate noisy pathways for the input question, thereby focusing specifically on pertinent factual knowledge. Moreover, we introduce Knowformer, a parameter-efficient method for injecting the re-scored knowledge graph into large language models to enhance their ability to perform factual reasoning. Extensive experiments on multiple KGQA benchmarks demonstrate the superiority of our method over existing systems.
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