AI-Cinema: A Hybrid Framework for Arabic Movie Scenario Generation With Traditional Storytelling and Cultural Dialogs

AI-Cinema is a hybrid neural-symbolic framework addressing the critical challenge of preserving cultural authenticity in Arabic movie scenario generation. The framework integrates transformer-based neural language models (AraT5-base and AraGPT2-medium) with symbolic reasoning encoded in OWL-DL ontologies and SWRL rules to ensure linguistic fluency, narrative coherence, and cultural preservation. AI-Cinema introduces a three-tier architecture comprising a data layer, cultural embedding layer, and scenario generation layer. Central to its design is an attention-based cultural embedding mechanism leveraging ArabicVerbNet (12,500 culturally annotated verbs) and ArabicNameNet (3653 names with regional annotations), complemented by a mathematically grounded harmony function that dynamically balances neural generation with symbolic constraints. In experiments on 2740 Arabic narratives, AI-Cinema achieves a BLEU-4 score of 32.76 (± 0.6), representing a 5.0% relative improvement over AraBERT-Gen (p