OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models

2026-07-02

AI Summary

This study addresses the fragmentation in ontology learning caused by the absence of a unified infrastructure, which has led to disjointed methodologies, domain-specific approaches, and inconsistent evaluation practices. To overcome these challenges, this work proposes a modular, cross-domain ontology learning framework that integrates ontology access, a large language model (LLM)-driven learning pipeline, and standardized benchmarking. The project establishes the first comprehensive resource comprising 180 machine-readable ontologies spanning 22 domains and introduces standardized datasets for three core ontology learning tasks: term typing, taxonomy discovery, and non-taxonomic relation extraction. A collaborative approach combining LLMs and retrieval models is employed to support these tasks. Large-scale experiments reveal that performance bottlenecks stem primarily from mismatches between knowledge encoding and ontology structure rather than model capacity, while also demonstrating the efficacy of cross-domain, multi-task evaluation as a foundation for systematic ontology learning research.

Abstract

Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices. Despite decades of research, OL lacks a shared infrastructure for systematic evaluation and ontology access. This absence has hindered progress and fragmented research, leaving the central challenges of OL largely unaddressed. We introduce OntoLearner, a modular, cross-domain, and first-of-its-kind framework that unifies ontology access, large language model (LLM)-driven learning pipelines, and standardized benchmarking. OntoLearner releases 180 machine-readable ontologies spanning 22 domains and provides pipeline-ready datasets with train/dev/test splits for three core OL tasks: term typing, taxonomy discovery, and non-taxonomic relation extraction. Using this infrastructure, we conduct a large-scale empirical study of OL, evaluating 22 retrieval models and 12 LLMs across domains and tasks. The results converge on a finding that reframes the central challenge of OL: failure modes scale with ontological complexity rather than model size or architectural sophistication. The primary bottleneck is not model capability, but a structural mismatch between how models encode knowledge and how ontologies organize it. These findings establish that effective OL is reachable through the cross-domain, multi-task benchmarking enabled by OntoLearner. OntoLearner is open-source (MIT license) at https://github.com/sciknoworg/OntoLearner/.

Problem

• Ontology Learning
• Knowledge Representation
• Large Language Models
• Benchmarking
• Structured Knowledge

Innovation

• Ontology Learning
• Large Language Models
• Modular Framework
• Cross-domain Benchmarking
• Knowledge Representation