An Ontology-Guided Multi-Anchor Graph Retrieval Framework for Traffic Legal Liability Determination

• URL: http://arxiv.org/abs/2606.11910v1
• Date: Wed, 10 Jun 2026 10:40:20 GMT
• Title: An Ontology-Guided Multi-Anchor Graph Retrieval Framework for Traffic Legal Liability Determination
• Abstract summary: Traffic law liability determination is critical for assigning legal penalties.<n>Existing retrieval-augmented generation methods suffer from a multi-dimensional retrieval bottleneck.<n>We propose OMAGR, an ontology-guided framework that decomposes queries into ontology-aligned anchors and executes parallel graph retrieval across each dimension.
• Score: 7.859898571932284
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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