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.

本研究解决了普通大众在获取法律知识时面临的技术性挑战和理解不足的问题。提出的QBR方法利用问题库作为桥梁，通过训练样本的衍生来提升知识单位的嵌入效果，从而实现更精准的细粒度法律知识检索。研究表明，QBR在传统方法中具有更高的准确性和效率，能更好地帮助公民解决日常法律问题，具有显著的社会影响。

基于问题库的细粒度法律知识检索方法