# Learning Description Logic Ontologies. Five Approaches. Where Do They Stand?

arXiv: https://arxiv.org/abs/2104.01193 · HTML: https://mlsanity.com/paper/2104.01193/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 56 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples](https://mlsanity.com/paper/1711.09576/) (2017)
- [From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules](https://mlsanity.com/paper/1805.10461/) (2018)
- [Exact Learning of Lightweight Description Logic Ontologies](https://mlsanity.com/paper/1709.07314/) (2017)
- [Mining EL Bases with Adaptable Role Depth](https://mlsanity.com/paper/2102.10689/) (2021)
- [On the Complexity of Learning Description Logic Ontologies](https://mlsanity.com/paper/2103.13694/) (2021)

## Built on by (most cited first, 12 of 12)

- [Towards Ontology Construction with Language Models](https://mlsanity.com/paper/2309.09898/) (2023)
- [Neural Class Expression Synthesis](https://mlsanity.com/paper/2111.08486/) (2021)
- [Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis](https://mlsanity.com/paper/2403.17216/) (2024)
- [Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies](https://mlsanity.com/paper/2105.08326/) (2021)
- [Automated and Explainable Ontology Extension Based on Deep Learning: A Case Study in the Chemical Domain](https://mlsanity.com/paper/2109.09202/) (2021)
- [Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies](https://mlsanity.com/paper/2204.14172/) (2022)
- [On the Power and Limitations of Examples for Description Logic Concepts](https://mlsanity.com/paper/2412.17345/) (2024)
- [Fitting Description Logic Ontologies to ABox and Query Examples](https://mlsanity.com/paper/2508.08007/) (2025)
- [Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration](https://mlsanity.com/paper/2308.06411/) (2023)
- [Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology](https://mlsanity.com/paper/2306.07662/) (2023)
- [Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Case Study on BERT-based Language Models](https://mlsanity.com/paper/2412.10513/) (2024)
- [Interpolation in Knowledge Representation](https://mlsanity.com/paper/2512.08833/) (2025)

## Abstract (arXiv)

The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.