Computational Analysis and Ontology Construction of Legal Texts: Theory, Methods, Practice Based on Topic Modeling

This research develops a framework for transforming legal texts into knowledge graphs via topic-driven ontology construction, integrating Latent Dirichlet Allocation with OWL/RDF formalization.Methodology: (1) text preprocessing, (2) LDA-based topic discovery, (3) topic-to-ontology translation, (4) instance mapping, (5) relationship establishment, (6) validation. Technical implementation uses OWL/RDF for specification and SPARQL for querying.Applications include intelligent legal question-answering, automated conflict detection, and legislative impact analysis. Integration of topic modeling with ontology engineering reduces annotation costs while maintaining fidelity. Domain-specific models (LEGAL-BERT) enhance performance but require expert validation.This work bridges statistical analysis and formal representation, advancing legal infrastructure from documents to graph-based systems.