# Learning Concept Lengths Accelerates Concept Learning in ALC

arXiv: https://arxiv.org/abs/2107.04911 · HTML: https://mlsanity.com/paper/2107.04911/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 10 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [A Semantic Matching Energy Function for Learning with Multi-relational Data](https://mlsanity.com/paper/1301.3485/) (2013)
- [Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction](https://mlsanity.com/paper/1307.7973/) (2013)
- [Convolutional Complex Knowledge Graph Embeddings](https://mlsanity.com/paper/2008.03130/) (2020)
- [Efficient Concept Induction for Description Logics](https://mlsanity.com/paper/1812.03243/) (2018)
- [Explaining Trained Neural Networks with Semantic Web Technologies: First Steps](https://mlsanity.com/paper/1710.04324/) (2017)

## Built on by (most cited first, 10 of 10)

- [Neural Class Expression Synthesis](https://mlsanity.com/paper/2111.08486/) (2021)
- [LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals](https://mlsanity.com/paper/2304.14742/) (2023)
- [Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2405.12654/) (2024)
- [Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings](https://mlsanity.com/paper/2207.08544/) (2022)
- [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)
- [Neural Reasoning for Robust Instance Retrieval in SHOIQ](https://mlsanity.com/paper/2510.20457/) (2025)
- [Explaining ELH Concept Descriptions through Counterfactual Reasoning](https://mlsanity.com/paper/2301.05109/) (2023)
- [Improving rule mining via embedding-based link prediction](https://mlsanity.com/paper/2406.10144/) (2024)
- [Explainable Benchmarking through the Lense of Concept Learning](https://mlsanity.com/paper/2510.20439/) (2025)

## Abstract (arXiv)

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.