Enhancing Moroccan legal cases analysis through ontology-based information extraction

Abstract

The efficient organization of diverse disorder cases within a unified memory necessitates an adaptable representation. This study introduces an ontology-based approach for extracting facts from Moroccan legal cases. Leveraging ontological frameworks, a comprehensive case architecture is established, enabling advanced information extraction. Utilizing rules, patterns, and knowledge modeling harmonizes cases and identifies pervasive legal concepts. Statistical techniques unveil latent entities within complex legal textual discourse. Empirical validation demonstrates proficiency, extracting up to 25 regular entities. The rule-based mechanism achieves an F1-score of 99.5%, highlighting precision, while the statistical extractor achieves an 88.3% F1-score, revealing concealed entities. This work presents an innovative ontology-based paradigm for legal information extraction, contributing to advanced knowledge management in the legal domain.