Email linqika@nus.edu.sg

Lin Qika is now a research fellow in NUS Saw Swee Hock School of Public Health interested. He is interested in general technologies of natural language processing and their applications for intelligent healthcare. Recently, large language models have achieved significant success in the AI community, and he intends to construct effective and robust healthcare applications based on them.

• NUS Saw Swee Hock School of Public Health

• Natural Language Processing
• Healthcare with AI
• Multi-modal representation and reasoning
• Neuro-symbolic system

• PhD. Xi’an Jiaotong University, China, 2023
• M.S. Beijing Institute of Technology, China, 2019
• B.S. Beijing Institute of Technology, China, 2016

• Lin Q, Liu J, Mao R, et al. TECHS: Temporal logical graph networks for explainable extrapolation reasoning[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023: 1281-1293.
• Lin Q, Mao R, Liu J, et al. Fusing topology contexts and logical rules in language models for knowledge graph completion[J]. Information Fusion, 2023, 90: 253-264.
• Lin Q, Liu J, Zhang L, et al. Contrastive graph representations for logical formulas embedding[J]. IEEE Transactions on Knowledge and Data Engineering, 2023.
• Lin Q, Liu J, Xu F, et al. Incorporating context graph with logical reasoning for inductive relation prediction[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022: 893-903.
• Lin Q, Liu J, Pan Y, et al. Rule-enhanced iterative complementation for knowledge graph reasoning[J]. Information Sciences, 2021, 575: 66-79.
• Lin Q, Zhu Y, Lu H, et al. Improving university faculty evaluations via multi-view knowledge graph[J]. Future Generation Computer Systems, 2021, 117: 181-192.
• Xu F, Liu J, Lin Q, et al. Logiformer: a two-branch graph transformer network for interpretable logical reasoning[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022: 1055-1065.
• Zhu Y, Cong F, Zhang D, et al. WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023: 3650-3662.
• Ma Y, Mao R, Lin Q, et al. Quantitative stock portfolio optimization by multi-task learning risk and return[J]. Information Fusion, 2024, 104: 102165.
• Ma Y, Mao R, Lin Q, et al. Multi-source aggregated classification for stock price movement prediction[J]. Information Fusion, 2023, 91: 515-528.