Center for Applied Mathematics, Tianjin University（天津大学应用数学中心）；Insitute of AI, Zhejiang Lab（浙江实验室人工智能研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Rujing Yao, Yingchun Ye, Ji Zhang, Shuxiao Li, Ou Wu
The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets and metrics are used as AI markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the entity extraction model is used in this study to extract AI markers from large-scale AI literature. Secondly, original papers are traced for AI markers. Statistical and propagation analysis are performed based on tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters and the influencing relationships amongst different research scene clusters are explored. The above-mentioned mining based on AI markers yields many meaningful discoveries. For example, the propagation of effective methods on the datasets is rapidly increasing with the development of time; effective methods proposed by China in recent years have increasing influence on other countries, whilst France is the opposite. Saliency detection, a classic computer vision research scene, is the least likely to be affected by other research scenes.
ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining
The Chinese University of Hong Kong, New Territories, Hong Kong SAR（香港中文大学）；Zhejiang Lab, Zhejiang University, Hangzhou, China（浙江大学杭州实验室）；Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China（中国科学院杭州医学研究所）
Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature
Argonne National Laboratory（阿贡国家实验室）；NVIDIA（英伟达）；University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；The University of Chicago（芝加哥大学）
COVID-19 therapy target discovery with context-aware literature mining
Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；University of Ljubljana（卢布尔雅那大学）；University of Nova Gorica（新戈里察大学）；Temida d.o.o（Temida有限责任公司）
A Systematic Literature Review about Idea Mining: The Use of Machine-driven Analytics to Generate Ideas
Stockholm University（斯德哥尔摩大学）
创意产生是创新的核心活动。数字数据源是创新的来源，如专利、出版物、社交媒体、网站等，正以前所未有的速度增长手动生成想法是耗时的，并且受到所涉及的个人的主观性的影响。因此，使用机器驱动的数据分析技术来分析数据以产生想法，并通过服务用户来支持想法的产生是有用的。本研究的目的是研究最先进的机器驱动分析的想法生成和数据源，因此本研究的结果通常将作为选择技术和数据源的指导方针。进行了系统的文献综述，以识别来自IEEE、Scopus、Web of Science和Google Scholar的相关学术文献。我们选择了71篇文章，并对它们进行了主题分析。本研究的结果表明，通过机器驱动分析的想法生成应用文本挖掘，信息检索（IR），人工智能（AI），深度学习，机器学习，统计技术，自然语言处理（NLP），基于NLP的形态分析，网络分析和文献计量学来支持想法生成。结果包括通过机器驱动的想法分析产生想法的技术和程序列表。此外，在想法生成中使用的表征和算法进行了总结。今后，可以探索旨在产生想法的工具。
Predicting New Concept-Object Associations in Astronomy by Mining the Literature
Georgia Institute of Technology（佐治亚理工学院）；The Ohio State University（俄亥俄州立大学）；Harvard & Smithsonian（哈佛与史密森尼学会）；Oak Ridge National Laboratory（橡树岭国家实验室）；Argonne National Laboratory（阿贡国家实验室）
