This study introduces an automated framework for constructing ontologies of abstract concepts by integrating Large Language Models (LLMs) with semantic web technologies. The proposed system leverages advanced models, including ChatGPT-4, GPT-4, and Gemini-2.0 flash-exp, to extract entities and relationships from textual data, transforming them into structured ontologies represented in the Web Ontology Language (OWL). By adhering to semantic web standards, the framework ensures the creation of reusable, scalable, and interoperable ontologies that enable advanced applications. This methodology bridges the gap between unstructured data and structured cultural knowledge, enhancing the digital representation and understanding of cultural concepts. As a case study, the framework is applied to extract a cultural ontology from a Wikipedia page, demonstrating its effectiveness in converting unstructured textual data into structured knowledge.