Sheng-Uei Guan
Papers
1
Total Citations
53
H-Index
1
About
Sheng-Uei Guan is a leading researcher in artificial intelligence, natural language processing, and intelligent systems, with a particular focus on bridging human-computer communication through semantic parsing and knowledge representation. His most cited work, the 2022 paper on a "Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot," has garnered 53 citations and introduces the innovative Text2GQL task—a method that converts natural language user queries into Graph Query Language (GQL) for graph databases. This contribution advances semantic parsing by enabling more direct and efficient interactions between humans and machines, particularly in healthcare applications. Guan’s research has significant implications for developing intelligent chatbots that can understand complex medical questions and retrieve precise information from structured knowledge bases. His work demonstrates a strong impact in combining deep learning with knowledge graphs to solve real-world problems, making him a notable figure in the field of AI-driven conversational systems.
Research Focus
Key Achievements
Top Papers
- 1