Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies, however, is a complex and time-consuming endeavor. ChEBI is a well-known ontology in the field of chemistry, which provides a comprehensive resource for defining chemical entities and their properties. However, it covers only a small fraction of the rapidly growing knowledge in chemistry and does not provide references to the scientific literature. To address this, we propose a methodology that involves augmenting existing annotated text corpora with knowledge from Chebi and fine-tuning a large language model (LLM) to recognize chemical entities and their roles in scientific text. Our experiments demonstrate the effectiveness of our approach. By combining ontological knowledge and the language understanding capabilities of LLMs, we achieve high precision and recall rates in identifying both the chemical entities and roles in scientific literature. Furthermore, we extract them from a set of 8,000 ChemRxiv articles, and apply a second LLM to create a knowledge graph (KG) of chemical entities and roles (CEAR), which provides complementary information to ChEBI, and can help to extend it.
Accurate Portraits of Scientific Resources and Knowledge Service Components
Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia（智能电信软件与多媒体北京重点实验室）；School of Computer Science (National Pilot School of Software Engineering)（计算机学院）；Beijing University of Posts and Telecommunications（北京邮电大学）
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。
Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain
University of Cagliari（卡利亚里大学）；FIZ Karlsruhe - Leibniz Institute for Information Infrastructure（FIZ卡尔斯鲁厄——莱布尼茨信息基础设施研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；The Open University（开放大学）；CNRS（法国国家科学研究中心）；University Paris 13（巴黎第十三大学）
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization
Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）；Advanced Research in Cyber Systems, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室先进网络系统研究部）；Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County（马里兰大学巴尔的摩分校计算机科学与电气工程系）
PaperRobot: Incremental Draft Generation of Scientific Ideas
Rensselaer Polytechnic Institute（伦斯勒理工学院）；DiDi Labs（滴滴实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Washington（华盛顿大学）
Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs
LAION；Open-Ψ (Open-Sci) Collective（Open-Ψ（Open-Sci）集体）；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Stanford University（斯坦福大学）；TIB—Leibniz Information Centre for Science and Technology（莱布尼茨科技信息中心）；Juelich Supercomputing Center（于利希超级计算中心）；Technical University of Munich（慕尼黑工业大学）
STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA（麦戈文脑科学研究所，麻省理工学院，马萨诸塞州剑桥市）；Fylo Labs Inc., New York, NY, USA（Fylo实验室公司，纽约州纽约市）；Allen Institute for Brain Science, Seattle, WA, USA（艾伦脑科学研究所，华盛顿州西雅图市）
