Silva, Ivanovitch Medeiros Dantas daOliveira, Gisliany Lillian Alves de2025-12-182025-12-182025-08-29OLIVEIRA, Gisliany Lillian Alves de. A Knowledge Graph-Based approach for modeling legislative texts: representation and document similarity analysis. Orientador: Dr. Ivanovitch Medeiros Dantas da Silva. 2025. 206f. Tese (Doutorado em Engenharia Elétrica e de Computação) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2025.https://repositorio.ufrn.br/handle/123456789/67273The most prominent task of the Legislative Branch — lawmaking — depends on a complex and demanding process in which new proposals must be examined, debated, and revised in light of existing legislation. These activities are often labor-intensive for humans due to the technical language, substantial length, and interdependence of legal texts. At the same time, these characteristics present a tangible opportunity for Artificial Intelligence (AI), particularly through the integration of Natural Language Processing (NLP) and structured data representations. Aiming to model legislative documents in a way that preserves their rich structural semantics, this work proposes an approach for transforming legislative texts into domain-specialized Knowledge Graphs (KGs) that capture their inherent hierarchical organization. The proposed method, based on LexML standards — a Brazilian XML schema for legal documents — extracts explicit structural relationships (e.g., articles, paragraphs, items) and augments them with contextual entities and relationships extracted by a Large Language Model (LLM). The resulting KGs, stored in a Neo4j database, capture both the internal topology of legal texts and their semantic nuances, enabling structured representations that support more meaningful analysis than unstructured raw text. To assess the effectiveness of this approach, comparative experiments were conducted on document similarity tasks, a core component of legislative workflows. Three scenarios were evaluated: (i) a text-only baseline using BERT-based sentence embeddings averaged across document sections; (ii) structure-aware KGs encoded via FastRP and GraphSAGE embeddings; and (iii) contextually enriched KGs also encoded via FastRP and GraphSAGE embeddings. Results using legislative proposals from the Legislative Assembly of Rio Grande do Norte (ALRN) show that while the text-based model achieved the highest precision, recall, and F1-scores, the KG-based representations provided interpretable, structure-driven insights. Contextual enrichment improved FastRP’s performance over structure-only graphs, while GraphSAGE performed best with structure-only representations, suggesting that LLM-derived relations may have introduced semantic noise for this graph model. Although the KGs were inherently heterogeneous, homogeneous graph algorithms were applied for simplicity, which may have limited performance. Nevertheless, the results demonstrate the feasibility of converting legislative documents to KGs, and the inclusion of structural information in embeddings was achieved, demonstrating potential for future improvements via heterogeneous models, advanced pooling strategies, or self-supervised pre-training. By bridging NLP and graph-based AI, this work advances approaches for legal document modeling, offering a pipeline for document similarity analysis and improving the legislative process efficiency.enAcesso AbertoTextos legislativosProcessamento de linguagem naturalGrafos de conhecimentoIA baseada em grafosGrandes modelos de linguagemdoctoralThesisENGENHARIAS::ENGENHARIA ELETRICA