TSOTSALearning at LLMs4OL Tasks A and B : Combining Rules to Large Language Model for Ontology Learning

Abstract

This paper presents our contribution to the Large Language Model For Ontology Learning (LLMs4OL) challenge hosted by ISWC conference. The challenge involves extracting and classifying various ontological components from multiple datasets. The organizers of the challenge provided us with the train set and the test set. Our goal consists of determining in which conditions foundation models such as BERT can be used for ontologies learning. To achieve this goal, we conducted a series of experiments on various datasets. Initially, GPT-4 was tested on the wordnet dataset, achieving an F1-score of 0.9264. Subsequently, we performed additional experiments on the same dataset using BERT. These experiments demonstrated that by combining BERT with rule-based methods, we achieved an F1-score of 0.9938, surpassing GPT-4 and securing the first place for term typing on the Wordnet dataset.