Abol Froushan, Fellow of the Royal Society of Arts, UK

Poetry presents unique challenges for natural language processing (NLP) due to its fragmented structure, intertextuality, and multimodal nature. Conventional NLP models struggle to capture its evolving semantic relationships, particularly across translations, historical contexts, and interpretative traditions. This paper introduces the Poetic Ontology Dataset (POD), a structured resource designed to embed poetic meaning as a dynamic, topological construct rather than a static textual entity. By applying sheaf theory, functorial mappings, and graph embeddings, we model poetic motifs and metaphors as interdependent structures within a meta-body-a network of poetic relations spanning time and cultures. Empirical validation compares AI-assisted meta-body analysis, derived through structured motif tracking and graph clustering, against traditional NLP embeddings. The results demonstrate that ontology-aware NLP models preserve semantic continuity and intertextual depth more effectively than conventional approaches. This work establishes a foundation for meaning-aware NLP architectures, bridging computational poetics, multimodal embeddings, and topological data analysis.

Computational Poetics, Ontology-Based NLP, Sheaf Theory in NLP, Functorial Mapping in AI, Poetic Meaning Representation, Graph-Theoretic Poetic Analysis