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Abstract
This paper presents a novel computational framework for formalizing and analyzing philosophical aesthetics discourse through the integration of knowledge representation techniques and large language models (LLMs). The framework introduces a structure based on four elements combining source texts, core ontology, knowledge graphs, and interpretative mappings to systematically capture both explicit content and implicit semantic relationships in aesthetics texts. Through a case study analyzing works by Dewey, Danto, and Sibley, we demonstrate how this approach enables the systematic reverse engineering of aesthetic analytical methods while maintaining semantic stability and interpretative clarity. The framework leverages human expert annotation alongside LLM capabilities to generate parallel knowledge representations, enabling comparative analysis of different interpretative approaches. Our experimental results reveal complementary strengths between human-annotated and LLM-generated ontologies, suggesting promising directions for hybrid approaches to philosophical knowledge representation. This research contributes to both digital humanities and computational philosophy by demonstrating how modern AI techniques can complement traditional philosophical analysis while addressing core challenges of semantic drift and interpretative bias in aesthetic discourse.