Nanopublication

ID

https://w3id.org/np/RAhp4Od3unROf7DJPsjqZLmiOvxgUjPOXTm7sa6QflEzM
Formats

Content

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sub:assertion {
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    dct:description "Generative AI is transforming ontology engineering. Large Language Models (LLMs) can assist with tasks such as terminology extraction, concept discovery, taxonomy construction, relationship identification, ontology documentation, and ontology alignment, significantly accelerating the development of semantic models. At the same time, AI-generated ontologies often contain conceptual inconsistencies, incorrect abstractions, hidden assumptions, and other modeling errors that require careful human review. This hands-on tutorial explores how generative AI can be effectively integrated into the ontology engineering lifecycle. Through demonstrations and practical exercises, participants will learn how to use LLMs to support and accelerate ontology development, recognize common failure modes, and apply established semantic modeling principles to validate and improve AI-generated artifacts.";
    schema:endDate "2026-09-15T17:30:00+02:00"^^xsd:dateTime;
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    rdfs:label "Using LLMs as Ontology Engineers: Methods, Workflows, and Pitfalls";
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  <https://orcid.org/0000-0001-9488-1870> foaf:name "Daniel Mietchen" .
  
  <https://orcid.org/0000-0002-1267-0234> foaf:name "Tobias Kuhn" .
  
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