13:40 〜 14:00
[2K4-IS-1a-01] Synthetic Data Generation Using GANs and LLM with Knowledge Graph: Application in Real-World Multi-Domain Datasets
キーワード:generative AI, synthetic data, knowledge graph
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.
講演PDFパスワード認証
論文PDFの閲覧にはログインが必要です。参加登録者の方は「参加者用ログイン」画面からログインしてください。あるいは論文PDF閲覧用のパスワードを以下にご入力ください。