Large language models (LLMs) have revolutionized human-machine interaction with their ability to converse and perform various language tasks. This study investigates the potential of LLMs for knowledge formalization using well-defined vocabularies, specifically focusing on OntoLex-Lemon. As a preliminary exploration, we test four languages (English, Italian, Albanian, Romanian) and analyze the formalization quality of nine words with varying characteristics using state-of-the-art metrics in RDF. While manual validation provided initial insights, it highlights the need for developing scalable evaluation methods for future large-scale experiments. This research aims to initiate a discussion on the potential and challenges of utilizing LLMs for knowledge formalization within the Semantic Web framework.
Evaluating Large Language Models for Linguistic Linked Data Generation
Maria Pia di Buono
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2024-01-01
Abstract
Large language models (LLMs) have revolutionized human-machine interaction with their ability to converse and perform various language tasks. This study investigates the potential of LLMs for knowledge formalization using well-defined vocabularies, specifically focusing on OntoLex-Lemon. As a preliminary exploration, we test four languages (English, Italian, Albanian, Romanian) and analyze the formalization quality of nine words with varying characteristics using state-of-the-art metrics in RDF. While manual validation provided initial insights, it highlights the need for developing scalable evaluation methods for future large-scale experiments. This research aims to initiate a discussion on the potential and challenges of utilizing LLMs for knowledge formalization within the Semantic Web framework.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
