LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph· 大模型辅助的本体工程和法国法律知识图构建
Maintenance regulations are complex legal texts that are difficult to exploit when addressing a specific case and challenging to integrate into operational systems. This paper presents a two-stage LLM-assisted workflow for French maintenance regulations: ontology engineering from a SEMLEG-based core ontology, followed by construction of an ontology-grounded French legal knowledge graph. The first stage consists in the open extraction of typed entities and triples from a stratified corpus sample, the normalization of labels through embedding-based fusion, and the induction of candidate object properties with their signature (domain and range). The second stage uses the resulting ontology to guide the closed extraction of triples and RDF graph construction over the full corpus. Experiments w
用大模型辅助构建法国法律知识图谱。
- 核心方法
- 两阶段大模型辅助的工作流程：第一阶段从分层语料库样本中提取实体和三元组，并通过基于嵌入的融合规范标签，生成对象属性的候选者；第二阶段使用生成的本体，指导从完整语料库中进行封闭式三元组提取与RDF图构建。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 法国法律文本复杂，难以应用于具体案例和整合进操作系统的挑战。
- 关键实验
- 未提供具体实验数据，但介绍了方法在法国赡养法规文本上的应用效果。
- 主要贡献
- 提出了一个有效的大模型辅助的法律本体工程与知识图构建方法，提高了法国法律文本处理与应用的效率。
- 意义与局限
- 本研究不仅有助于提高法律文本的处理效率，还为未来类似领域的知识图构建提供了参考方法。局限在于该方法可能需要针对不同法律领域的语料库进行调整和优化。