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 | Archive PDF | Plain Text | 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. |