Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；GreenDynamics（绿动力）；Department of Materials Science and Engineering, City University of Hong Kong（香港城市大学材料科学与工程系）；Department of Linguistics and Translation, City University of Hong Kong（香港城市大学语言学与翻译系）；School of Photovoltaic and Renewable Energy Engineering, University of New South Wales（新南威尔士大学光伏与可再生能源工程学院）；College of Design & Innovation, Tongji University（同济大学设计与创新学院）
Yanpeng Ye, Jie Ren, Shaozhou Wang, Yuwei Wan, Imran Razzak, Bram Hoex, Haofen Wang, Tong Xie, Wenjie Zhang
Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges to the efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovation. Addressing these challenges, the integration of artificial intelligence with materials science has opened avenues for accelerating the discovery process, though it also demands precise annotation, data extraction, and traceability of information. To tackle these issues, this article introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural language processing techniques integrated with large language models to extract and systematically organize a decade's worth of high-quality research into structured triples, contains 162,605 nodes and 731,772 edges. MKG categorizes information into comprehensive labels such as Name, Formula, and Application, structured around a meticulously designed ontology, thus enhancing data usability and integration. By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods. This structured approach not only streamlines materials research but also lays the groundwork for more sophisticated science knowledge graphs.
Psychomatics -- A Multidisciplinary Framework for Understanding Artificial Minds
Catholic University of Sacred Heart（圣心天主教大学）；Istituto Auxologico Italiano IRCCS（意大利奥索洛吉科研究所）；University of Milano - Bicocca（米兰比可卡大学）；Virtual Reality Medical Center（虚拟现实医疗中心）；Interactive Media Institute（交互媒体研究所）
MDK12-Bench: A Comprehensive Evaluation of Multimodal Large Language Models on Multidisciplinary Exams
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；USTC（中国科学技术大学）；RIT（罗切斯特理工学院）；HIT（哈尔滨工业大学）；WHU（武汉大学）；MBZUAI（马克斯·普朗克人工智能研究所）；NUS（新加坡国立大学）
A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models
State Key Laboratory of Fluid Power Transmission and Control（流体动力传动与控制国家重点实验室）；School of Aeronautics and Astronautics（航空宇航科学与技术学院）；China Ship Development and Design Center（船舶设计发展研究中心）
MMMG: A Massive, Multidisciplinary, Multi-Tier Generation Benchmark for Text-to-Image Reasoning
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；The University of Electro-Communications（电气通信大学）；Australian National University（澳大利亚国立大学）；Microsoft Research Asia（微软亚洲研究院）；Microsoft（微软）
SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions
TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心（TIB））；University of Brescia（布雷西亚大学）；University of Alicante（阿利坎特大学）；Georgia Institute of Technology（佐治亚理工学院）；Duke University（杜克大学）；Johns Hopkins University（约翰霍普金斯大学）；University of Manchester（曼彻斯特大学）；Narnia Labs（纳尼亚实验室）
VISTA Architect: A graph database-oriented health AI system demonstrated in multidisciplinary tumor boards
Department of Biomedical Data Science, Stanford University School of Medicine（斯坦福大学医学院生物医学数据科学系）；Department of Medicine, Stanford University School of Medicine（斯坦福大学医学院医学系）
Overview of STEM Science as Process, Method, Material, and Data Named Entities
TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学技术信息中心）
我们面临着世界范围内学术出版物空前的产量。数字图书馆的利益相关者认为，以文件为基础的出版模式已经达到了适当性的极限。相反，结构化的，机器可解释的，细粒度的学术知识出版作为知识图谱（KG）的强烈主张。在这项工作中，我们开发和分析了10个不同学科的STEM文章的大规模结构化数据集，即农业，天文学，生物学，化学，计算机科学，地球科学，工程，材料科学，数学和医学。我们的分析是定义在一个大型语料库，包括60 K摘要结构为四个科学实体的过程，方法，材料和数据。因此，我们的研究提出了，第一次，一个大规模的多学科语料库的分析下的四个命名实体标签，专门定义和选择是领域独立的，而不是特定领域的建设。然后，这项工作无意中是一个可行性测试的特点多学科科学领域独立的概念。此外，为了总结每个学科每个概念的科学知识的不同方面，提供了一组词云可视化。在这项工作中创建的STEM-NER-60 k语料库包括从主要出版平台获得的60 k STEM文章中提取的超过100万个实体，并公开发布https://github.com/jd-coderepos/stem-ner-60k上。
