LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph
2026-07-27 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created a two-step method to help understand and organize complicated French maintenance laws using large language models (LLMs). First, they built a structured map (ontology) from a sample of legal texts by identifying important concepts and their relationships. Then, they used this map to extract detailed information from the entire set of laws, creating a knowledge graph. Their experiments showed this approach effectively grouped similar terms and uncovered new relationships, which could improve how legal rules are managed in practical settings.
ontology engineeringlegal knowledge graphlarge language modelstriple extractionlabel normalizationembedding fusionsemantic webRDFpredicatedomain-range signature
Authors
G{é}nesis Montenegro, Mokhtar Boumedyen Billami, Catherine Faron, Fabien Gandon, Pierre Monnin
Abstract
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 with GPT-4.1 and mistral-large-2512 show robust structured outputs, near-complete class alignment, and a substantial reduction of duplicated entities and predicates after fusion. Fewer than 20% of triples introduce unseen properties, while lower exact signature compliance reveals new domain-range combinations for existing predicates. These results point to predicate normalization and the validation of newly observed relation signatures as key refinement steps for industrial maintenance settings.