---
_id: '31257'
abstract:
- lang: eng
  text: Concept learning approaches based on refinement operators explore partially
    ordered solution spaces to compute concepts, which are used as binary classification
    models for individuals. However, the number of concepts explored by these approaches
    can grow to the millions for complex learning problems. This often leads to impractical
    runtimes. We propose to alleviate this problem by predicting the length of target
    concepts before the exploration of the solution space. By these means, we can
    prune the search space during concept learning. To achieve this goal, we compare
    four neural architectures and evaluate them on four benchmarks. Our evaluation
    results suggest that recurrent neural network architectures perform best at concept
    length prediction with a macro F-measure ranging from 38% to 92%. We then extend
    the CELOE algorithm, which learns ALC concepts, with our concept length predictor.
    Our extension yields the algorithm CLIP. In our experiments, CLIP is at least
    7.5 times faster than other state-of-the-art concept learning algorithms for ALC---including
    CELOE---and achieves significant improvements in the F-measure of the concepts
    learned on 3 out of 4 datasets. For reproducibility, we provide our implementation
    in the public GitHub repository at https://github.com/dice-group/LearnALCLengths
author:
- first_name: N’Dah Jean
  full_name: Kouagou, N’Dah Jean
  last_name: Kouagou
- first_name: Stefan
  full_name: Heindorf, Stefan
  last_name: Heindorf
- first_name: Caglar
  full_name: Demir, Caglar
  last_name: Demir
- first_name: Ngonga Axel-Cyrille
  full_name: Ngomo, Ngonga Axel-Cyrille
  last_name: Ngomo
citation:
  ama: 'Kouagou NJ, Heindorf S, Demir C, Ngomo NA-C. Learning Concept Lengths Accelerates
    Concept Learning in ALC. In: <i>ESWC</i>. Vol 13261. Springer; 2022:236-252.'
  apa: Kouagou, N. J., Heindorf, S., Demir, C., &#38; Ngomo, N. A.-C. (2022). Learning
    Concept Lengths Accelerates Concept Learning in ALC. <i>ESWC</i>, <i>13261</i>,
    236–252.
  bibtex: '@inproceedings{Kouagou_Heindorf_Demir_Ngomo_2022, title={Learning Concept
    Lengths Accelerates Concept Learning in ALC}, volume={13261}, booktitle={ESWC},
    publisher={Springer}, author={Kouagou, N’Dah Jean and Heindorf, Stefan and Demir,
    Caglar and Ngomo, Ngonga Axel-Cyrille}, year={2022}, pages={236–252} }'
  chicago: Kouagou, N’Dah Jean, Stefan Heindorf, Caglar Demir, and Ngonga Axel-Cyrille
    Ngomo. “Learning Concept Lengths Accelerates Concept Learning in ALC.” In <i>ESWC</i>,
    13261:236–52. Springer, 2022.
  ieee: N. J. Kouagou, S. Heindorf, C. Demir, and N. A.-C. Ngomo, “Learning Concept
    Lengths Accelerates Concept Learning in ALC,” in <i>ESWC</i>, Hersonissos, Crete,
    Greece, 2022, vol. 13261, pp. 236–252.
  mla: Kouagou, N’Dah Jean, et al. “Learning Concept Lengths Accelerates Concept Learning
    in ALC.” <i>ESWC</i>, vol. 13261, Springer, 2022, pp. 236–52.
  short: 'N.J. Kouagou, S. Heindorf, C. Demir, N.A.-C. Ngomo, in: ESWC, Springer,
    2022, pp. 236–252.'
conference:
  end_date: 2022-06-02
  location: Hersonissos, Crete, Greece
  name: Extended Semantic Web Conference (ESWC)
  start_date: 2022-05-29
date_created: 2022-05-17T09:55:25Z
date_updated: 2023-06-27T10:23:10Z
department:
- _id: '34'
intvolume: '     13261'
keyword:
- dice knowgraphs raki daikiri kouagou heindorf demir ngonga
language:
- iso: eng
main_file_link:
- url: http://arxiv.org/abs/2107.04911
page: 236 - 252
publication: ESWC
publisher: Springer
related_material:
  link:
  - relation: confirmation
    url: http://arxiv.org/abs/2107.04911
status: public
title: Learning Concept Lengths Accelerates Concept Learning in ALC
type: conference
user_id: '87189'
volume: 13261
year: '2022'
...