Title of the Report:Knowledge Graph Inference Under Low-Resource Conditions

Presenter:Li Yuling

Affiliation: Anhui Medical University

Date of the Report:July 13, 2024 (Saturday), 15:40-16:25

Location of the Report: Second Conference Room on the First Floor, Block A, Feicui Science and Education Building

Abstract:With the advent of the big data era, knowledge graphs have become an important tool for representing and organizing knowledge. In many fields, especially in medicine, law, and scientific research, obtaining a large amount of labeled data is very difficult and expensive. Therefore, conducting effective knowledge inference with a small number of samples has significant practical application value. Based on this background, the recent major research findings are presented, including methods for few-shot knowledge inference for multiple semantic relations and methods for few-shot knowledge inference for multiple mapping relations. Finally, based on these research results, the significance and challenges of achieving few-shot knowledge inference under medical knowledge graphs are discussed.

   Biography of the Presenter:Li Yuling, female, PhD, associate professor at Anhui Medical University, master's supervisor. Her main research areas include medical data analysis, knowledge graphs, and natural language processing. She has published more than ten papers in journals and international conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE/ACM Transactions on Audio, Speech, and Language Processing, and EMNLP, obtained five national invention patents, and presided over the PhD research startup fund for introduced talents at Anhui Medical University.