  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.
