Actively Learning Ontologies from LLMs: First Results (Extended Abstract)

Active learning is a method where a learner attempts to learn some kind of knowledge by posing questions to a teacher. In computational learning theory, classically, the questions made by the learner are called membership queries and are answered with ‘yes’ or ‘no’ (or equivalently, with ‘true’ or ‘false’). Here we consider that the teacher is a language model and study the case in which the knowledge is expressed as an ontology. We present preliminary results showing the performance of GPT and other language models when answering whether concept inclusions created by an ontology engineer on prototypical ℰℒ ontologies are ‘true’ or ‘false’.

parole chiave   Active Learning, Ontologies, Language Models

evento origine

 DL 2024

rivista o collana

 CEUR Workshop Proceedings (CEUR-WS.org)

sostituita da

 Actively Learning EL Terminologies from Large Language Models (articolo in atti, 2025) — Matteo Magnini, Riccardo Squarcialupi, Martin T. Sterri, Ana Ozaki