Advancing Thesaurus Construction With Generative AI: A Structured Approach to Synonym Identification and Scope Note Development

Generative Artificial Intelligence (GAI) has emerged as a transformative tool for knowledge organization, offering new possibilities for thesaurus construction through automated synonym identification and scope note generation. This study investigates the integration of GAI into thesaurus development and proposes a structured AI-human collaborative framework to enhance lexical precision and contextual accuracy. Using Confucius Ceremony cultural vocabulary as a case study, the research evaluates AI’s ability to identify synonym relationships and generate structured scope notes through prompt engineering combined with expert validation. An iterative experimental design was adopted, incorporating gold sample testing, structured prompt frameworks, and expert-guided refinements to optimize AI-generated outputs. Results indicate that AI, when directed by structured prompts, can efficiently identify core synonyms and generate semantically rich scope notes. However, challenges persist in contextual reasoning, historical lexicon interpretation, and cultural nuance recognition. The findings highlight the importance of ontology-based reasoning, explicit metadata integration, and domain-specific knowledge graphs in improving AI’s applicability to knowledge organization systems (KOS). This study contributes to AI-assisted thesaurus development by outlining best practices in prompt engineering and human-AI collaboration for multilingual and historically complex terminologies. Future research should explore AI’s adaptability across broader domains, including structured scientific thesauri and indigenous knowledge systems while advancing automated evaluation methods and transparency mechanisms for AI-generated lexicographic content. The findings underscore the necessity of integrating AI capabilities with human expertise to ensure semantic accuracy, cultural sensitivity, and ethical integrity in AI-driven knowledge organization.