The Artificial Intelligence Ontology: LLM-assisted construction of AI
  concept hierarchies
Marcin P. Joachimiak Mark A. Miller J. Harry Caufield Ryan Ly Nomi L. Harris Andrew Tritt Christopher J. Mungall Kristofer E. Bouchard
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).