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.

翻译：交通法律责任判定对于分配法律处罚至关重要，需要同时识别跨多个法律维度的相互依存的法定条款。然而，现有的检索增强生成方法面临多维检索瓶颈：单轴架构将复杂的法律查询压缩为单一通道，导致相互依存的法定维度被忽视。为此，我们提出了OMAGR——一种本体引导的框架，该框架将查询分解为与本体对齐的锚点，并在每个维度上执行并行图检索，确保在融合前各维度独立检索。为评估所提方法，我们创建了TrafficLaw-QA数据集，这是一个经专家验证的基准数据集，包含200个问题与527条法律条款。实验结果表明，TrafficOmni-RAG在上下文精确度与忠实度指标上优于基线方法。研究证明，并行多锚点检索能有效解决多维检索瓶颈，为交通法律责任判定研究提供了有前景的方向。