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).

人工智能本体论（AIO）是一个人工智能（AI）概念、方法论及其相互关系的系统化体系。通过手动整理和大语言模型（LLMs）的辅助开发，AIO 旨在通过提供一个全面框架，涵盖技术和伦理两个方面的人工智能技术，来应对人工智能领域迅速发展的局面。

人工智能本体论：基于 LLM 辅助构建的人工智能概念层次结构

The Artificial Intelligence Ontology: LLM-assisted construction of AI  concept hierarchies

Predicting the emergence of links in large evolving networks is a difficult
task with many practical applications. Recently, the Science4cast competition
has illustrated this challenge presenting a network of 64.000 AI concepts and
asking the participants to predict which topics are going to be researched
together in the future. In this paper, we present a solution to this problem
based on a new family of deep learning approaches, namely Graph Neural
Networks. The results of the challenge show that our solution is competitive
even if we had to impose severe restrictions to obtain a computationally
efficient and parsimonious model: ignoring the intrinsic dynamics of the graph
and using only a small subset of the nodes surrounding a target link.
Preliminary experiments presented in this paper suggest the model is learning
two related, but different patterns: the absorption of a node by a sub-graph
and union of more dense sub-graphs. The model seems to excel at recognizing the
first type of pattern.

本文提出了一种基于 Graph Neural Networks 的新型深度学习方法，以解决如何预测大型演化网络中链接出现的问题，特别地，预测与 AI 相关的主题之间的链接。实验结果表明，使用此方法能够有效地识别出节点的吸收和密集子图的合并两种模式，且模型具有较高的预测准确性。