用多锚点图检索解决交通法律责任判定中的多维信息遗漏问题
An Ontology-Guided Multi-Anchor Graph Retrieval Framework for Traffic Legal Liability Determination
- 将法律查询分解为对齐本体的锚点，分维度并行检索
- 在200个问题上实现上下文精度和忠实度双提升
- 适合法律AI、智能司法系统研究者参考
交通法律责任判定对处罚裁量至关重要，需同时识别跨多个法律维度的相互依赖条文。然而现有检索增强生成方法存在多维检索瓶颈：单一轴线架构将复杂法律查询压缩为单一路径，导致各维度间关联被忽略。为此，我们提出OMAGR框架，通过本体引导将查询分解为对齐锚点，在各维度执行并行图检索，确保维度间独立检索后再融合。为评估该方法，我们构建了TrafficLaw-QA数据集，一个专家验证的基准数据集，包含200个问题和527条法律条文。实验表明，TrafficOmni-RAG在上下文精度和忠实度指标上优于基线模型。结果证明，平行多锚点检索能有效突破多维检索瓶颈，为交通法律责任判定研究提供新方向。
原文摘要 · Abstract (English)
Traffic law liability determination is critical for assigning legal penalties, requiring the simultaneous identification of interdependent statutory provisions across multiple legal dimensions. However, existing retrieval-augmented generation methods suffer from a multi-dimensional retrieval bottleneck: single axis architectures compress complex legal queries into a single pathway, causing interdependent statutory dimensions to be overlooked. To address this, we propose OMAGR, an ontology-guided framework that decomposes queries into ontology-aligned anchors and executes parallel graph retrieval across each dimension, ensuring independent retrieval across dimensions before fusion. To evaluate the proposed method, we created the TrafficLaw-QA dataset, an expert-validated benchmark dataset containing 200 questions and 527 legal provisions. Results show that TrafficOmni-RAG outperforms baselines on Context Precision and Faithfulness metrics. The findings demonstrate that parallel multi-anchor retrieval effectively resolves the multi-dimensional retrieval bottleneck, offering a promising direction for traffic law liability determination research.
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