{ "@context": { "dcterms": "http://purl.org/dc/terms/", "dcmitype": "http://purl.org/dc/dcmitype/" }, "dcterms:identifier": [ "tag:aim-pro.eu,2026:oer/c458cdf0adfe", "https://arxiv.org/abs/2404.03044", "arxiv:2404.03044" ], "dcterms:title": [ "The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies" ], "dcterms:type": [ { "@id": "dcmitype:Text" }, "preprint" ], "dcterms:creator": [ "Marcin P. Joachimiak", "Mark A. Miller", "J. Harry Caufield", "Ryan Ly", "Nomi L. Harris", "Andrew Tritt", "Christopher J. Mungall", "Kristofer E. Bouchard" ], "dcterms:description": [ "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)." ], "dcterms:subject": [ "cs.LG (arxiv)", "cs.AI (arxiv)" ], "dcterms:language": [ "en" ], "dcterms:license": [ { "@id": "http://creativecommons.org/licenses/by/4.0/" } ], "dcterms:rights": [ "CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive" ], "dcterms:accessRights": [ "open" ], "dcterms:format": [ "application/pdf", "text/markdown" ], "dcterms:extent": [ "517516 bytes (original)", "37604 bytes (extracted text)" ], "dcterms:issued": [ "2024-04-03" ], "dcterms:provenance": [ "Retrieved from arXiv on 2026-10-09 in response to the search string “(all:\"artificial intelligence\" OR all:\"machine learning\" OR all:\"generative AI\" OR all:\"deep learning\" OR all:\"reinforcement learning\" OR all:\"large language model\") AND (all:\"AI concepts\" OR all:\"types of AI\" OR all:\"AI fundamentals\" OR all:\"recognizing AI\" OR all:\"recognising AI\" OR all:\"general versus narrow AI\" OR all:\"narrow AI\" OR all:\"general AI\" OR all:\"machine intelligence\" OR all:\"AI strengths and weaknesses\" OR all:\"traditional software\" OR all:\"rule-based systems\" OR all:\"introduction to AI\" OR all:\"introduction to artificial intelligence\" OR all:\"artificial intelligence introduction\" OR all:\"AI primer\" OR all:\"foundations of artificial intelligence\" OR all:\"overview of AI\" OR all:\"understanding AI\" OR all:\"history of AI\" OR all:\"AI essentials\" OR all:\"AI terminology\" OR all:\"metaphors for AI\" OR all:\"AI fundamental concepts\" OR all:\"AI key concepts\" OR all:\"philosophy of AI\" OR all:\"critical AI literacy\")”. arXiv served the resource and is not asserted to be its publisher or author.", "Text extracted from pdf to Markdown by pdf; 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