# Efficient Concept Induction for Description Logics

arXiv: https://arxiv.org/abs/1812.03243 · HTML: https://mlsanity.com/paper/1812.03243/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

## Built on by (most cited first, 17 of 17)

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- [Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies](https://mlsanity.com/paper/2204.14172/) (2022)
- [Explaining Deep Learning Hidden Neuron Activations using Concept Induction](https://mlsanity.com/paper/2301.09611/) (2023)
- [Understanding CNN Hidden Neuron Activations Using Structured Background Knowledge and Deductive Reasoning](https://mlsanity.com/paper/2308.03999/) (2023)
- [Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python](https://mlsanity.com/paper/2510.11561/) (2025)
- [Learning Permutation-Invariant Embeddings for Description Logic Concepts](https://mlsanity.com/paper/2303.01844/) (2023)
- [Reverse Engineering of Temporal Queries Mediated by LTL Ontologies](https://mlsanity.com/paper/2305.01248/) (2023)
- [A Case Study on Concept Induction for Neuron-Level Interpretability in CNN](https://mlsanity.com/paper/2603.00197/) (2026)
- [Explaining ELH Concept Descriptions through Counterfactual Reasoning](https://mlsanity.com/paper/2301.05109/) (2023)
- [ConceptLens: from Pixels to Understanding](https://mlsanity.com/paper/2410.05311/) (2024)
- [Semantics-Aware Caching for Concept Learning](https://mlsanity.com/paper/2603.06506/) (2026)
- [Do CNNs Internally Represent Real and Fake Images Differently? A Hidden-Layer Analysis](https://mlsanity.com/paper/2608.14729/) (2026)

## Abstract (arXiv)

Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e., ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.