Abstract
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 (this https URL) and BioPortal (this https URL).
Abstract (translated)
人工智能知识图谱（AIO）是一个对人工智能（AI）概念、方法和它们之间相互关系的系统化。AIO是通过手动策展开发起来的，并得到了大型语言模型（LLMs）的额外帮助。它旨在通过提供一个全面涵盖AI技术的技术和道德方面的框架，来应对AI领域快速变化的地形。AIO的主要受众包括AI研究人员、开发人员和教育者，他们寻求在AI领域使用标准化的术语和概念。AIO围绕六个顶级分支展开：网络、层、功能、LLMs、预处理和偏见，每个分支都旨在支持AI方法的模块化组合，并促进对深度学习架构和AI伦理问题的更深入理解。AIO的开发利用了知识图谱开发工具（ODK）进行创建和维护，其内容通过AI驱动的策展支持进行动态更新。这种方法不仅确保了AIO在AI快速发展的大背景下保持其相关性，而且显著提高了研究人员、开发人员和教育者的使用价值，通过简化新AI概念和方法的集成来提高其实用性。AIO的知识有用性通过将AI方法数据注释在AI研究出版物目录中，并将其集成到BioPortal元数据资源中，突出其跨学科研究潜力得到了充分证明。AIO是开源的，可以在GitHub（https://github.com）和BioPortal（https://biportal.org）上获取。
URL
https://arxiv.org/abs/2404.03044
