Combining Semantic Embeddings and Knowledge Graphs for Identifying Decision Patterns in Brazilian Judicial Decisions

Gustavo Soares Silva, Omar Andres Carmona Cortes, Fábio Manoel França Lobato, Antonio Fernando Lavareda Jacob Junior


Abstract
Approaches based solely on textual representations have limitations in capturing structural relations between legal entities, particularly in documents with high lexical similarity. This paper presents ongoing work on a dynamic clustering system for judicial decisions that integrates hybrid representations, combining semantic embeddings from legal-domain Portuguese models with knowledge graphs automatically constructed from documents. The architecture supports incremental clustering and generates cluster justifications using Large Language Models grounded on knowledge graph relations. Preliminary evaluation combines the quantitative metrics Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index.
Anthology ID:
2026.propor-2.25
Volume:
Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2
Month:
April
Year:
2026
Address:
Salvador, Brazil
Editors:
Marlo Souza, Iria de-Dios-Flores, Diana Santos, Larissa Freitas, Jackson Wilke da Cruz Souza, Eugénio Ribeiro
Venue:
PROPOR
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
181–185
Language:
URL:
https://aclanthology.org/2026.propor-2.25/
DOI:
Bibkey:
Cite (ACL):
Gustavo Soares Silva, Omar Andres Carmona Cortes, Fábio Manoel França Lobato, and Antonio Fernando Lavareda Jacob Junior. 2026. Combining Semantic Embeddings and Knowledge Graphs for Identifying Decision Patterns in Brazilian Judicial Decisions. In Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2, pages 181–185, Salvador, Brazil. Association for Computational Linguistics.
Cite (Informal):
Combining Semantic Embeddings and Knowledge Graphs for Identifying Decision Patterns in Brazilian Judicial Decisions (Silva et al., PROPOR 2026)
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PDF:
https://aclanthology.org/2026.propor-2.25.pdf