@inproceedings{silva-etal-2026-combining-semantic,
title = "Combining Semantic Embeddings and Knowledge Graphs for Identifying Decision Patterns in {B}razilian Judicial Decisions",
author = "Silva, Gustavo Soares and
Cortes, Omar Andres Carmona and
Lobato, F{\'a}bio Manoel Fran{\c{c}}a and
Junior, Antonio Fernando Lavareda Jacob",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 2",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-2.25/",
pages = "181--185",
ISBN = "979-8-89176-387-6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Combining Semantic Embeddings and Knowledge Graphs for Identifying Decision Patterns in Brazilian Judicial Decisions
%A Silva, Gustavo Soares
%A Cortes, Omar Andres Carmona
%A Lobato, Fábio Manoel França
%A Junior, Antonio Fernando Lavareda Jacob
%Y Souza, Marlo
%Y de-Dios-Flores, Iria
%Y Santos, Diana
%Y Freitas, Larissa
%Y Souza, Jackson Wilke da Cruz
%Y Ribeiro, Eugénio
%S Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2
%D 2026
%8 April
%I Association for Computational Linguistics
%C Salvador, Brazil
%@ 979-8-89176-387-6
%F silva-etal-2026-combining-semantic
%X 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.
%U https://aclanthology.org/2026.propor-2.25/
%P 181-185
Markdown (Informal)
[Combining Semantic Embeddings and Knowledge Graphs for Identifying Decision Patterns in Brazilian Judicial Decisions](https://aclanthology.org/2026.propor-2.25/) (Silva et al., PROPOR 2026)
ACL