Learning Concept Lengths Accelerates Concept Learning in ALC
This work addresses runtime inefficiencies in concept learning for ALC, offering a domain-specific improvement that is incremental but yields significant speed gains.
The paper tackles the problem of slow runtimes in concept learning for ALC by predicting concept lengths to prune the search space, resulting in an algorithm (CLIP) that is at least 7.5 times faster than state-of-the-art methods and improves F-measure on 3 out of 4 datasets.
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.