Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python
Authors: Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Blübaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo
JMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we present Ontolearn a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent stateof-the-art symbolic and neuro-symbolic class expression learners including Evo Learner and DRILL. ... Ontolearn has already been applied in industrial projects, where ante-hoc explainability is required. ... Ontolearn is a well-tested framework that comes with 156 unit and regression tests along with 95% test coverage. |
| Researcher Affiliation | Academia | Caglar Demir EMAIL... Axel-Cyrille Ngonga Ngomo EMAIL Department of Computer Science Paderborn University Warburger Str. 100, 33098 Paderborn, Germany. ... Simon Bin EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It includes architectural diagrams and examples, but no formal algorithmic descriptions. |
| Open Source Code | Yes | The source code of Ontolearn is available at https://github.com/dice-group/Ontolearn. |
| Open Datasets | No | The paper mentions working with 'large knowledge graphs' and provides 'A partial visualization of the Family knowledge base along with a learning problem defined by E+ and E' in Figure 2 as an example. However, it does not provide concrete access information (link, DOI, citation) for any specific dataset used or to the Family knowledge base mentioned. |
| Dataset Splits | No | The paper describes a framework for learning OWL class expressions but does not detail any experimental setup with specific dataset splits (e.g., training/test/validation percentages or counts) for experiments performed within this paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models, processor types, or memory amounts) used for running its experiments or for developing the Ontolearn framework. |
| Software Dependencies | No | The paper mentions 'Python', 'Owlapy', 'OWL reasoners, e.g. Hermit (...) and Pellet (...)', 'LLMs like Llama (...) or Mistral (...), and 'Python s unittest framework'. However, it does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper describes the Ontolearn framework and its capabilities but does not provide specific experimental setup details, such as hyperparameter values, training configurations, or system-level settings for any new experiments conducted within this paper. |