<p>This study addresses the limitations of traditional keyword-based legal information systems by proposing a robust model for managing legislative changes. We introduce the Legal-Onto model, integrated with Vietnamese SBert, to enhance the semantic representation and accessibility of legal documents. The model leverages an ontology-based framework to represent legal structures and employs a Sentence-BERT variant tailored for Vietnamese to compare legal texts at multiple granularities–chapters, sections, articles, clauses, and points. Unlike prior methods that rely solely on the highest similarity score, our approach retains multiple high-similarity matches to mitigate misalignment caused by overlapping legal provisions. This strategy ensures accurate mapping of new legislative elements and prevents erroneous prioritization. The model architecture integrates relational and conceptual graphs with natural language processing techniques to systematically detect and highlight legislative updates. Experimental evaluations on the Vietnamese Land Laws of 2013 and 2024 demonstrate the model’s effectiveness in identifying both structural and content changes. Results show that the Legal-Onto model significantly improves legal knowledge management by achieving high alignment accuracy and reducing manual analysis efforts.</p>