🤖 AI Summary
This study addresses the challenge in existing retrieval-augmented generation (RAG) approaches for traffic legal liability determination, where modeling interdependent legal provisions across multiple legal dimensions remains difficult due to a multi-dimensional retrieval bottleneck. To overcome this limitation, the authors propose OMAGR, a novel framework that introduces, for the first time, an ontology-guided multi-anchor parallel graph retrieval mechanism. Specifically, the query is decomposed into multiple semantic anchors via ontology alignment, enabling parallel graph-based retrieval across distinct legal dimensions, followed by result fusion to enhance generation accuracy. This approach substantially mitigates information compression and omission issues inherent in joint retrieval of multi-dimensional legal provisions. Experimental results on the newly constructed TrafficLaw-QA dataset demonstrate that TrafficOmni-RAG significantly outperforms current baselines in both Context Precision and Faithfulness metrics.
📝 Abstract
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.