Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Community Challenges Overview and 2.1 CCKS 2. Community Challenges Overview and 2.1 CCKS 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 2.2 CHIP and 2.3 CCIR, CSMI, CCL and DCIC 3. Evaluation Tasks Overview and 3.1 Information Extraction 3. Evaluation Tasks Overview and 3.1 Information Extraction 3.2 Text Classification and Text Similarity 3.2 Text Classification and Text Similarity 3.3 Knowledge Graph and Question Answering 3.3 Knowledge Graph and Question Answering 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 3.4 Text Generation and Knowledge Reasoning and 3.5 Large Language Model Evaluation 4. Translational Informatics in Biomedical Text Mining 4. Translational Informatics in Biomedical Text Mining 5. Discussion and Perspective 5.1. Contributions of Community Challenges 5.1. Contributions of Community Challenges 5.2. Limitations of Current Community Challenges 5.2. Limitations of Current Community Challenges 5.3. Future Perspectives in the Era of Large Language Models, and References 5.3. Future Perspectives in the Era of Large Language Models, and References Figure Legends and Tables Figure Legends and Tables 3.3 Knowledge Graph and Question Answering The knowledge graph is a structured knowledge representation used to store and organize knowledge. It consists of a network of entities and their relationships. Knowledge graph have extensive applications in the field of biomedicine, it provides a way to organize and integrate diverse biomedical data from various sources, enabling researchers to explore complex relationships and make new discoveries. Biomedical knowledge question answering utilizes the biomedical knowledge graph and NLP techniques to enable computers to answer natural language questions related to biomedicine or healthcare, assisting doctors and researchers in more efficiently accessing and applying biomedical knowledge. In 2020, CCKS released the task of construction and question-answering of COVID-19 knowledge graph [56]. It defined four subtasks, including: 1) inference of entity types in the COVID-19 knowledge graph, 2) prediction of hierarchical relationships between entities, 3) link prediction, such as targeted effects of drugs and viruses or protein interactions, and 4) knowledge question answering: constructing question-answering data focused on specific subjects such as health, medicine, and disease prevention and control related to COVID-19. In 2021, CCKS released the task for link prediction in a multi-level knowledge graph of phenotypes, drugs, and molecules, aims to predict seven relationship categories [57]. In 2019, CCIR organized a task that provided a medical event graph built with EHRs, along with a series of natural language questions, requiring participants to build systems that can automatically return results for the given questions. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author. Authors: Authors: (1) Hui Zong, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (2) Rongrong Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and the author contributed equally; (3) Jiaxue Cha, Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Bio-Medicine, Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China; (4) Erman Wu, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China; (5) Jiakun Li, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China; (6) Liang Tao, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China; (7) Zuofeng Li, Takeda Co. Ltd., Shanghai, 200040, China; (8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; (9) Bairong Shen, Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China and a Corresponding author.
