EvoLearner: Learning Description Logics with Evolutionary Algorithms

S. Heindorf, L. Blübaum, N. Düsterhus, T. Werner, V.N. Golani, C. Demir, A.-C. Ngonga Ngomo, in: WWW, ACM, 2022, pp. 818–828.

Conference Paper | English
Heindorf, StefanLibreCat ; Blübaum, Lukas; Düsterhus, Nick; Werner, Till; Golani, Varun Nandkumar; Demir, CaglarLibreCat; Ngonga Ngomo, Axel-CyrilleLibreCat
Classifying nodes in knowledge graphs is an important task, e.g., predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high predictive performance, they are only post-hoc and locally explainable and do not allow the learned model to be easily enriched with domain knowledge. Towards this end, learning description logic concepts from positive and negative examples has been proposed. However, learning such concepts often takes a long time and state-of-the-art approaches provide limited support for literal data values, although they are crucial for many applications. In this paper, we propose EvoLearner - an evolutionary approach to learn ALCQ(D), which is the attributive language with complement (ALC) paired with qualified cardinality restrictions (Q) and data properties (D). We contribute a novel initialization method for the initial population: starting from positive examples (nodes in the knowledge graph), we perform biased random walks and translate them to description logic concepts. Moreover, we improve support for data properties by maximizing information gain when deciding where to split the data. We show that our approach significantly outperforms the state of the art on the benchmarking framework SML-Bench for structured machine learning. Our ablation study confirms that this is due to our novel initialization method and support for data properties.
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Heindorf S, Blübaum L, Düsterhus N, et al. EvoLearner: Learning Description Logics with Evolutionary Algorithms. In: WWW. ACM; 2022:818-828. doi:10.1145/3485447.3511925
Heindorf, S., Blübaum, L., Düsterhus, N., Werner, T., Golani, V. N., Demir, C., & Ngonga Ngomo, A.-C. (2022). EvoLearner: Learning Description Logics with Evolutionary Algorithms. WWW, 818–828. https://doi.org/10.1145/3485447.3511925
@inproceedings{Heindorf_Blübaum_Düsterhus_Werner_Golani_Demir_Ngonga Ngomo_2022, title={EvoLearner: Learning Description Logics with Evolutionary Algorithms}, DOI={10.1145/3485447.3511925}, booktitle={WWW}, publisher={ACM}, author={Heindorf, Stefan and Blübaum, Lukas and Düsterhus, Nick and Werner, Till and Golani, Varun Nandkumar and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2022}, pages={818–828} }
Heindorf, Stefan, Lukas Blübaum, Nick Düsterhus, Till Werner, Varun Nandkumar Golani, Caglar Demir, and Axel-Cyrille Ngonga Ngomo. “EvoLearner: Learning Description Logics with Evolutionary Algorithms.” In WWW, 818–28. ACM, 2022. https://doi.org/10.1145/3485447.3511925.
S. Heindorf et al., “EvoLearner: Learning Description Logics with Evolutionary Algorithms,” in WWW, 2022, pp. 818–828, doi: 10.1145/3485447.3511925.
Heindorf, Stefan, et al. “EvoLearner: Learning Description Logics with Evolutionary Algorithms.” WWW, ACM, 2022, pp. 818–28, doi:10.1145/3485447.3511925.
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