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

• URL: http://arxiv.org/abs/2607.01977v1
• Date: Thu, 02 Jul 2026 10:09:03 GMT
• Title: OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models
• Abstract summary: OntoLearner aims to automatically construct structured knowledge models from text.<n>Despite decades of research, OntoLearner lacks a shared infrastructure for systematic evaluation and access.<n>OntoLearner unifies access, large language model (LLM) pipelines, and standardized benchmarking.
• Score: 6.625264939050408
• License: http://creativecommons.org/licenses/by/4.0/
• 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/.

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