GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news

📄 arXiv: 2506.11600v1 📥 PDF

作者: Abdul Haque, Umm e Hani, Ahmad Din, Muhammad Babar, Ali Abbas, Insaf Ullah

分类: cs.IR, cs.AI

发布日期: 2025-06-13

备注: 18 pages, 8 figures

💡 一句话要点

提出GraphRAG-Causal框架以增强新闻因果推理能力

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 因果推理 图形检索 大型语言模型 知识图谱 虚假信息检测 新闻分析

📋 核心要点

  1. 现有的自然语言处理方法在识别复杂的隐含因果关系时存在不足，尤其是在数据稀缺的场景中。
  2. 论文提出了一种将图形检索与大型语言模型结合的框架，通过构建因果知识图谱来增强因果推理能力。
  3. 实验结果显示，该框架在因果分类任务中取得了82.1%的F1-score，显著提升了准确性和一致性。

📝 摘要（中文）

GraphRAG-Causal提出了一种创新框架，将图形检索与大型语言模型相结合，以增强新闻分析中的因果推理能力。传统的自然语言处理方法在识别复杂的隐含因果关系时常常面临挑战，尤其是在数据稀缺的情况下。该方法通过将注释的新闻标题转化为结构化的因果知识图谱来应对这些挑战，并利用混合检索系统结合语义嵌入和图形结构线索，准确匹配和检索相关事件。实验结果表明，GraphRAG-Causal在仅使用20个少量示例的情况下，因果分类的F1-score达到了82.1%。

🔬 方法详解

问题定义：本论文旨在解决传统自然语言处理方法在因果推理中面临的挑战，尤其是在数据稀缺情况下对复杂因果关系的识别不足。

核心思路：论文的核心思路是通过将新闻标题转化为结构化的因果知识图谱，结合图形检索与大型语言模型，提升因果推理的准确性。

技术框架：整体架构分为三个主要阶段：数据准备阶段将新闻句子注释并转化为因果图；图形检索阶段将图谱及其嵌入存储在Neo4j数据库中，并利用混合Cypher查询识别相关事件；LLM推理阶段利用检索到的因果图进行少量学习设置，进行因果关系的分类和标记。

关键创新：最重要的技术创新在于将图形结构与语义嵌入相结合的混合检索系统，显著提高了因果关系的匹配和检索效率。

关键设计：在设计中，采用了Neo4j数据库存储因果图及其嵌入，使用XML基础的提示进行少量学习，确保了分类和标记的鲁棒性。实验中仅使用20个示例即可达到较高的F1-score。

📊 实验亮点

实验结果显示，GraphRAG-Causal在因果分类任务中取得了82.1%的F1-score，显著高于传统方法。这一成果表明，该框架在数据稀缺情况下仍能有效识别因果关系，具有良好的实用性和可靠性。

🎯 应用场景

该研究具有广泛的应用潜力，尤其在新闻可靠性评估、虚假信息检测和政策分析等领域。通过增强因果推理能力，能够帮助决策者和研究人员更准确地理解事件之间的因果关系，从而提高信息的可信度和决策的有效性。

📄 摘要（原文）

GraphRAG-Causal introduces an innovative framework that combines graph-based retrieval with large language models to enhance causal reasoning in news analysis. Traditional NLP approaches often struggle with identifying complex, implicit causal links, especially in low-data scenarios. Our approach addresses these challenges by transforming annotated news headlines into structured causal knowledge graphs. It then employs a hybrid retrieval system that merges semantic embeddings with graph-based structural cues leveraging Neo4j to accurately match and retrieve relevant events. The framework is built on a three-stage pipeline: First, during Data Preparation, news sentences are meticulously annotated and converted into causal graphs capturing cause, effect, and trigger relationships. Next, the Graph Retrieval stage stores these graphs along with their embeddings in a Neo4j database and utilizes hybrid Cypher queries to efficiently identify events that share both semantic and structural similarities with a given query. Finally, the LLM Inference stage utilizes these retrieved causal graphs in a few-shot learning setup with XML-based prompting, enabling robust classification and tagging of causal relationships. Experimental evaluations demonstrate that GraphRAG-Causal achieves an impressive F1-score of 82.1% on causal classification using just 20 few-shot examples. This approach significantly boosts accuracy and consistency, making it highly suitable for real-time applications in news reliability assessment, misinformation detection, and policy analysis.