Keynote Speaker

 

Prof. Xi Chen, Nanjing University, China

 

Chair of Marketing and Electronic Business Department

 

Xi Chen, Ph.D., Full Professor, Ph.D. Supervisor, Chair of Marketing and Electronic Business Department, Nanjing University, Visiting Scholar of Michael G. Foster School of Business, University of Washington. He authors more than 110 refereed journal/international conference papers and book chapters, more than 90 papers among them have been indexed by SCI/SSCI and EI, including lots of papers in UTD list and JCR I section. He has published 3 monographs and owned several national authorized patents. He is the project chief and sub-project leader of more than 30 Foundations, including the key project of the National Natural Science Foundation, the key project of the National Social Sciences Foundation, National Science Foundation, and National Ministry of Education Science Foundation, and so on. He is the innovation team leader of Nanjing University. He got the Youth Award of Management in China. Best Paper Awards of the Chinese Academy of System Simulation and China Information Economics Society, Outstanding Achievement Award of Philosophy and Social Sciences of Jiangsu Province and so on. He has been elected to the Ministry of Education for New Century Excellent Talents Scheme since 2011, and 5 high-level talent plans in Jiangsu Province. He is the chair and the commissioner of some international/national academic associations, the chair of some technical committees, the chair of the organization committee of some refereed conferences, and the associate editor and editorial member of some refereed international journals and conferences. His research interests include business intelligence, services engineering and management, digital economy and complex systems.

 

Title: Knowledge Graph Construction Research in TCM

Abstract: This study presents the construction of a knowledge graph for acupuncture treatment of depression, integrating traditional Chinese medicine with advanced artificial intelligence technologies. Despite acupuncture's proven efficacy in alleviating depressive symptoms, its knowledge remains fragmented and lacks standardized semantic representation. To address this gap, we developed a domain-specific ontology using the Seven-Step Method, defining 11 core concepts. Leveraging large language models like ChatGPT, we extracted 12,148 triples from 1,263 screened academic papers, standardized entities using ICD-11 and TCM guidelines, and stored the KG in Neo4j for interactive querying and visualization.  Our results demonstrate the KG's utility in mapping acupoint-symptom relationships, supporting clinical decision-making, and facilitating research. Challenges included data heterogeneity and multilingual extraction, while innovations centered on AI-assisted KG construction and the fusion of TCM with modern data science. The combination of traditional Chinese medicine frameworks with modern AI and data science techniques represents an innovative fusion of old and new knowledge paradigms. The innovation can be widely applied in e-learning and training.