The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies
<|reference_start|>The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies: 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).<|reference_end|>
@article{joachimiak2024the,
    title={The Artificial Intelligence Ontology: LLM-assisted construction of AI
  concept hierarchies},
    author={Marcin P. Joachimiak, Mark A. Miller, J. Harry Caufield, Ryan Ly, Nomi
  L. Harris, Andrew Tritt, Christopher J. Mungall, Kristofer E. Bouchard},
    journal={arXiv preprint arXiv:2404.03044},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03044},
    primaryClass={cs.LG cs.AI}
}