---
id: 20260327-T0-13
title: "S-Path-RAG：提升多跳知识图谱问答的语义感知检索方法"
title_en: "S-Path-RAG Boosts Multi-Hop Knowledge Graph QA"
url: https://ai.daily.yangsir.net/daily/20260327-T0-13
issue_date: 2026-03-27
publish_date: 2026-03-26T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.23512
---

# S-Path-RAG：提升多跳知识图谱问答的语义感知检索方法

研究人员提出S-Path-RAG框架，专门解决大知识图谱中的多跳问答问题。该方法通过枚举有限语义路径，替代传统的一次性文本检索，显著提升复杂问题回答准确性。测试显示，S-Path-RAG在处理需要多次推理的问题时，比基线模型减少30%的检索错误，为开发者构建更可靠的知识图谱问答系统提供新工具。

## English Version

**S-Path-RAG Boosts Multi-Hop Knowledge Graph QA**

Researchers propose S-Path-RAG, a semantic-aware framework that improves multi-hop question answering over large knowledge graphs. By enumerating bounded semantic paths instead of one-shot text retrieval, the method reduces errors by 30% in complex reasoning tasks compared to baselines, offering developers a more reliable tool for knowledge graph QA systems.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.23512)

**详情页**：https://ai.daily.yangsir.net/daily/20260327-T0-13

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