Semantic Role Mining and Hierarchical Role Generation Based on Formal Concept Analysis

• Tao Wang,
• Qiang Wu,

Vol. 20, No. 3, March 31, 2026
10.3837/tiis.2026.03.001, Download Paper (Free):

• Semantic role mining
• Hierarchical role generation
• Role-based access control (RBAC)
• Concept lattice
• Multi-objective optimization

Abstract

Role mining is a critical component of Role-Based Access Control (RBAC) systems, enabling the automated extraction of roles from user-permission relationships. However, traditional methods often fail to capture semantic richness and struggle to generate hierarchical roles that align with organizational structures. To address these limitations, this study proposes a novel approach, Semantic Role Mining and Hierarchical Role Generation Based on Formal Concept Analysis (FCA). The proposed methodology leverages FCA to construct concept lattices from user-permission matrices, capturing inherent semantic relationships and extracting semantically coherent roles. Hierarchical roles are generated by analyzing the lattice's natural structure, while a multi-objective optimization algorithm ensures a balance between role quality, hierarchy depth, and computational efficiency. The approach is rigorously evaluated on real-world datasets, including healthcare and enterprise systems, using metrics such as role coherence, coverage, and hierarchy quality. Experimental results demonstrate that the proposed method significantly outperforms traditional techniques in terms of role interpretability, semantic consistency, and alignment with organizational structures, thereby enhancing the manageability and security of RBAC systems. This study provides a robust and scalable framework for semantic role mining and hierarchical role generation, offering a practical solution for complex access control environments. Future research will explore dynamic role mining and the integration of machine learning techniques to further improve adaptability and efficiency.

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