IRIS at LLMs4OL 2025 Tasks B, C and D: Enhancing Ontology Learning Through Data Enrichment and Type Filtering

Abstract

Ontology Learning (OL) automates extracting structured knowledge from unstructured data. We study how model-agnostic data manipulations can boost performance of Large Language Models (LLM) on three OL tasks, i.e., term typing, taxonomy discovery and non-taxonomic relation extraction, from the LLMs4OL 2025 Challenge. We investigate two input‐enrichment techniques, i.e., (i) data augmentation, and (ii) addition of term and type definitions that expand the information supplied to an LLM. Complementing the enrichment techniques, we also study a pruning technique, i.e., a similarity-based candidate filtering technique that narrows the candidate space in taxonomy discovery and non-taxonomic relation extraction to the most semantically relevant types. When applied individually, each technique boosts precision–recall metrics over the vanilla setting where an LLM is trained on the original data. However, applied together they yield the best scores in five out of the seven ontology–task combinations, showing synergetic benefits. Our findings show that careful curation of inputs can itself yield substantial performance improvements. Codebase and all training artifacts are available at our GitHub repository.