# On the Complexity of Learning Description Logic Ontologies

arXiv: https://arxiv.org/abs/2103.13694 · HTML: https://mlsanity.com/paper/2103.13694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples](https://mlsanity.com/paper/1711.09576/) (2017)
- [Learning Deterministic Weighted Automata with Queries and Counterexamples](https://mlsanity.com/paper/1910.13895/) (2019)
- [The DL-Lite Family and Relations](https://mlsanity.com/paper/1401.3487/) (2014)
- [Exact Learning of Lightweight Description Logic Ontologies](https://mlsanity.com/paper/1709.07314/) (2017)
- [Learning Ontologies with Epistemic Reasoning: The EL Case](https://mlsanity.com/paper/1902.03273/) (2019)

## Built on by (most cited first, 1 of 1)

- [Learning Description Logic Ontologies. Five Approaches. Where Do They Stand?](https://mlsanity.com/paper/2104.01193/) (2021)

## Abstract (arXiv)

Ontologies are a popular way of representing domain knowledge, in particular, knowledge in domains related to life sciences. (Semi-)automating the process of building an ontology has attracted researchers from different communities into a field called "Ontology Learning". We provide a formal specification of the exact and the probably approximately correct learning models from computational learning theory. Then, we recall from the literature complexity results for learning lightweight description logic (DL) ontologies in these models. Finally, we highlight other approaches proposed in the literature for learning DL ontologies.