{"ID":5675284,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01977","arxiv_id":"2607.01977","title":"OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models","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/.","short_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...","url_abs":"https://arxiv.org/abs/2607.01977","url_pdf":"https://arxiv.org/pdf/2607.01977v1","authors":"[\"Hamed Babaei Giglou\",\"Jennifer D'Souza\",\"Andrei Aioanei\",\"Nandana Mihindukulasooriya\",\"Sören Auer\"]","published":"2026-07-02T10:09:03Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":613893,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-03T01:40:09.565152011Z","DeletedAt":null,"paper_id":5675284,"paper_url":"https://arxiv.org/abs/2607.01977","paper_title":"OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models","repo_url":"https://github.com/sciknoworg/OntoLearner","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}