The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；UC Berkeley（加州大学伯克利分校）；Helen Wills Neuroscience Institute（海伦·威尔斯神经科学研究所）；Redwood Center for Theoretical Neuroscience（红杉理论神经科学中心）
Marcin P. Joachimiak, Mark A. Miller, J. Harry Caufield, Ryan Ly, Nomi L. Harris, Andrew Tritt, Christopher J. Mungall, Kristofer E. Bouchard
人工智能本体（AIO）是对人工智能（AI）概念、方法论及其相互关系的系统化整理。AIO通过人工策展开发，并借助大语言模型（LLMs）的额外辅助，旨在通过提供一个涵盖AI技术技术和伦理两方面的综合框架，来应对快速演变的AI领域。AIO的主要受众包括AI研究人员、开发者和教育工作者，他们需要在AI领域内寻求标准化的术语和概念。该本体围绕六个顶层分支构建：Networks、Layers、Functions、LLMs、Preprocessing和Bias，每个分支旨在支持AI方法的模块化组合，并促进对深度学习架构和AI伦理考量的更深入理解。AIO的开发使用了Ontology Development Kit（ODK）进行创建和维护，其内容通过AI驱动的策展支持动态更新。这种方法不仅确保了本体在AI快速进展中的相关性，而且通过简化新AI概念和方法论的整合，显著增强了其对研究人员、开发者和教育工作者的实用性。该本体的实用性通过AI研究出版物目录中AI方法数据的标注以及集成到BioPortal本体资源中得到展示，突显了其跨学科研究的潜力。AIO本体是开源的，可在GitHub（https://github.com/berkeleybop/artificial-intelligence-ontology）和BioPortal（https://bioportal.bioontology.org/ontologies/AIO）上获取。
英文摘要：
The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. The ontology is structured around six top-level branches: Networks, Layers, Functions, LLMs, Preprocessing, and Bias, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI.
AIO's development utilized the Ontology Development Kit (ODK) for its creation and maintenance, with its content being dynamically updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies.
The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub (https://github.com/berkeleybop/artificial-intelligence-ontology) and BioPortal (https://bioportal.bioontology.org/ontologies/AIO).
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Leipzig University（莱比锡大学）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））；School of Embedded Composite Artificial Intelligence (SECAI)（嵌入式复合人工智能学院（SECAI））；TU Dortmund University（多特蒙德工业大学）
An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example
School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气工程与电信学院）；Faculty of Medicine, Ludwig Maximilian University of Munich（慕尼黑大学医学院）；The First Clinical Medical College, Zhejiang Chinese Medicine University（浙江中医药大学第一临床医学院）
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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）；Faculty of Sciences, Engineering and Technology, The University of Adelaide（阿德莱德大学科学、工程与技术学院）；School of Information Systems and Technology Management, University of New South Wales（新南威尔士大学信息系统与技术管理学院）
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University of Manchester（曼彻斯特大学）；Jönköping University（延雪平大学）；National University of Singapore（新加坡国立大学）；COMPtech i Skillingaryd AB（COMPtech i Skillingaryd AB公司）
Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs
Università degli Studi di Milano（米兰大学）；Università della Svizzera italiana (USI)（瑞士意大利语大学）；Politecnico di Milano（米兰理工大学）；Aalto University（阿尔托大学）；FGV（杰图利奥·瓦加斯基金会）
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School of Computer Science & Informatics, University of Liverpool（利物浦大学计算机科学与信息学院）；Unilever Plc. Materials Innovation Factory, University of Liverpool（联合利华公司材料创新工厂，利物浦大学）
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Institute for Parallel and Distributed Systems, University of Stuttgart（并行与分布式系统研究所，斯图加特大学）；Institute of Polymer Chemistry, University of Stuttgart（聚合化学研究所，斯图加特大学）；Institute of Biochemistry, University of Stuttgart（生物化学研究所，斯图加特大学）
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InESC-ID Lisboa（里斯本INESC-ID）；Instituto Superior Técnico（技术学院）；Faculdade de Medicina Dentária da Universidade de Lisboa（里斯本大学牙医学院）；FCiências.ID - Centro de Estatística e Aplicações da Universidade de Lisboa (CEAUL)（里斯本大学FCiências.ID）；Instituto Politécnico de Leiria - Escola Superior de Tecnologia e Gestão（莱里亚技术学院）；IST-ID；Instituto Superior de Engenharia de Lisboa（里斯本工程学院）
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Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-Universität Bochum（波鸿鲁尔大学先进材料模拟跨学科中心）；Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；BAM Federal Institute for Materials Research and Testing（BAM联邦材料研究与测试研究所）
Authoring and Management of Transparent Research Integrity Assessments of Randomised Clinical Trial Publications Using LLM-assisted Tools and Provenance Knowledge Graphs
Interdisciplinary Institute, University of Aberdeen（阿伯丁大学跨学科研究所）；University of Aberdeen（阿伯丁大学）；Indiana University（印第安纳大学）；University of Manchester（曼彻斯特大学）；Aberdeen Centre for Evaluation, University of Aberdeen（阿伯丁大学阿伯丁评估中心）；University of Auckland（奥克兰大学）
BoxLitE: A Faithful Knowledge Base Embedding Based on Convex Optimization
The Institute of Statistical Mathematics, Japan（日本统计数学研究所）；TU Wien, Austria（奥地利技术大学维也纳分校）；University of Oslo, Norway（挪威奥斯陆大学）；University of Applied Sciences Campus Vienna, Austria（奥地利应用科学大学维也纳校区）
