QBR: A Question-Bank-Based Approach to Fine-Grained Legal Knowledge Retrieval for the General Public

2025-05-08

AI Summary

The public faces significant challenges in effectively retrieving legal knowledge due to a semantic gap between the domain-specificity of legal texts and users’ limited legal expertise. Method: This paper proposes a Question Bank (QB)-driven legal knowledge retrieval framework. It introduces a structured question bank as an intermediary to construct fine-grained, query-to-knowledge-unit alignment training samples, jointly optimized with a semantic embedding model to enable interpretable knowledge localization—without requiring users to formulate expert-level queries. Contribution/Results: The approach simultaneously enhances retrieval accuracy, computational efficiency, and result interpretability. Experiments demonstrate statistically significant improvements over baseline methods in retrieval precision, response latency, and user comprehension accuracy. Validation across multiple real-world legal consultation scenarios confirms its practical applicability and societal value.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Retrieval of legal knowledge by the general public is a challenging problem due to the technicality of the professional knowledge and the lack of fundamental understanding by laypersons on the subject. Traditional information retrieval techniques assume that users are capable of formulating succinct and precise queries for effective document retrieval. In practice, however, the wide gap between the highly technical contents and untrained users makes legal knowledge retrieval very difficult. We propose a methodology, called QBR, which employs a Questions Bank (QB) as an effective medium for bridging the knowledge gap. We show how the QB is used to derive training samples to enhance the embedding of knowledge units within documents, which leads to effective fine-grained knowledge retrieval. We discuss and evaluate through experiments various advantages of QBR over traditional methods. These include more accurate, efficient, and explainable document retrieval, better comprehension of retrieval results, and highly effective fine-grained knowledge retrieval. We also present some case studies and show that QBR achieves social impact by assisting citizens to resolve everyday legal concerns.

Problem

• Bridging gap between technical legal content and untrained users
• Improving fine-grained legal knowledge retrieval accuracy
• Enhancing public comprehension of legal document results

Innovation

• Uses Question Bank to bridge knowledge gap
• Enhances document embedding with training samples
• Improves fine-grained legal knowledge retrieval