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

arXiv: https://arxiv.org/abs/2404.03044 · HTML: https://mlsanity.com/paper/2404.03044/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)](https://mlsanity.com/paper/2312.10904/) (2023)
- [A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks](https://mlsanity.com/paper/2110.01434/) (2021)
- [Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies](https://mlsanity.com/paper/2207.02056/) (2022)
- [Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist](https://mlsanity.com/paper/2311.02107/) (2023)

## Built on by (most cited first, 5 of 5)

- [OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs](https://mlsanity.com/paper/2607.17963/) (2026)
- [From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development](https://mlsanity.com/paper/2602.00699/) (2026)
- [Tipta uzmanlik sinavinda (tus) buyuk dil modelleri insanlardan daha mi basarili?](https://mlsanity.com/paper/2408.12305/) (2024)
- [From Ethical Declarations to Provable Independence: An Ontology-Driven Optimal-Transport Framework for Certifiably Fair AI Systems](https://mlsanity.com/paper/2510.08086/) (2025)
- [SAREF-based Ontology for Distributed AI Workflows across the Edge-Fog-Cloud Continuum](https://mlsanity.com/paper/2608.26160/) (2026)

## Abstract (arXiv)

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).