mlsanityData to 7 Oct 2026

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

arXiv 2104.01193 ↗First posted 2 Apr 2021cs.AI cs.LG cs.LOcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 6 of its 56 references that are in the map. First posted on arXiv 2 Apr 2021.

Builds on

6 of 56 references.

References · 6

  1. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  2. 2017Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamplescited by 66
  3. 2018From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rulescited by 19
  4. 2017Exact Learning of Lightweight Description Logic Ontologiescited by 10
  5. 2021Mining EL Bases with Adaptable Role Depthcited by 3
  6. 2021On the Complexity of Learning Description Logic Ontologiescited by 1

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 3; 2025: 2.

12 papers.

  1. 2023Towards Ontology Construction with Language Modelscited by 23
  2. 2021Neural Class Expression Synthesiscited by 6
  3. 2024Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysiscited by 4
  4. 2021Actively Learning Concepts and Conjunctive Queries under ELr-Ontologiescited by 3
  5. 2021Automated and Explainable Ontology Extension Based on Deep Learning: A Case Study in the Chemical Domaincited by 3
  6. 2022Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologiescited by 3
  7. 2024On the Power and Limitations of Examples for Description Logic Conceptscited by 2
  8. 2025Fitting Description Logic Ontologies to ABox and Query Examplescited by 2
  9. 2023Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alterationcited by 1
  10. 2023Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontologycited by 0
  11. 2024Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Case Study on BERT-based Language Modelscited by 0
  12. 2025Interpolation in Knowledge Representationcited by 0