Women’s familial roles—as mothers, wives, and daughters—are central to Islamic ethical and social thought, yet narrations addressing these roles in ?a??? al-Bukh?r? are dispersed across books and chapters, limiting their accessibility and pedagogical value. Recent scholarship affirms the importance of these roles for questions of authority, leadership, and family well-being, but highlights the absence of systematic frameworks to organize them thematically. This study introduces the Semantic Field Theory–Hadith Extraction Model (SFT-HEM), a conceptual framework for semantically clustering narrations on women’s familial roles. Grounded in Semantic Field Theory, the model employs lexical relations such as synonymy, hyponymy, and meronymy to move beyond keyword searches and rigid RDF taxonomies that miss semantic nuance. From an initial corpus of 1,463 narrations, manual curation and validation through classical commentaries refined the set to 60 role-specific narrations: 45% on mothers, 40% on wives, and 15% on daughters. By integrating classical semantics with computational tools such as Arabic WordNet and morphosyntactic parsers, SFT-HEM offers a linguistically grounded, theologically respectful approach that benefits educators, students, and digital humanists, while also laying the foundation for future automation in Arabic NLP and ontology-based retrieval.
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