Prediction of concept lengths for fast concept learning in description logics

• URL: http://arxiv.org/abs/2107.04911v1
• Date: Sat, 10 Jul 2021 21:00:48 GMT
• Title: Prediction of concept lengths for fast concept learning in description logics
• Authors: N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo
• Abstract summary: Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts. We propose a supervised machine learning approach for learning concept lengths, which allows predicting the length of the target concept. We show that integrating our concept length predictor into the CELOE algorithm improves CELOE's runtime by a factor of up to 13.4 without any significant changes to the quality of the results it generates.
• Score: 2.0474076605741036
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 refinement trees spanned by these approaches can easily grow to millions of nodes for complex learning problems. This leads to refinement-based approaches often failing to detect optimal concepts efficiently. In this paper, we propose a supervised machine learning approach for learning concept lengths, which allows predicting the length of the target concept and therefore facilitates the reduction of the search space during concept learning. To achieve this goal, we compare four neural architectures and evaluate them on four benchmark knowledge graphs--Carcinogenesis, Mutagenesis, Semantic Bible, Family Benchmark. Our evaluation results suggest that recurrent neural network architectures perform best at concept length prediction with an F-measure of up to 92%. We show that integrating our concept length predictor into the CELOE (Class Expression Learner for Ontology Engineering) algorithm improves CELOE's runtime by a factor of up to 13.4 without any significant changes to the quality of the results it generates. For reproducibility, we provide our implementation in the public GitHub repository at https://github.com/ConceptLengthLearner/ReproducibilityRepo

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