2025年度 人工知能学会全国大会（第39回）
2025年5月27日〜5月30日大阪国際会議場＋オンライン
2025年度 人工知能学会全国大会（第39回）
2025年5月27日〜5月30日大阪国際会議場＋オンライン
[2K4-IS-1a-01]Synthetic Data Generation Using GANs and LLM with Knowledge Graph: Application in Real-World Multi-Domain Datasets
〇Ke YU1, Shigeru Ishikura2, Yukari Usukura2, Yuki Shigoku2, Teruaki Hayashi1(1. the university of Tokyo, 2. Infomart Corporation)
This study propose the application of generative AI to create high-quality synthetic datasets that resemble four distinct real-world datasets, each representing a different domain. The proposed approach leverages the strengths of generative models, such as LLMs and GANs, combined with domain-specific knowledge encoded in knowledge graphs. The study aims to maintain the logical consistency, statistical distribution, and structural characteristics of the original datasets while ensuring data quality and usability. The results demonstrate the potential of generative AI to provide scalable, privacy-preserving synthetic datasets for research and practical applications across various fields, including geography, transactions, customer demographics, and contractual data.