Classifying nodes in knowledge graphs is an important task, e.g., for 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 concepts in , which is the attributive language with complement () paired with qualified cardinality restrictions () and data properties (). We contribute a novel initialization method for the initial population: starting from positive examples, 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.

Several quality dimensions of natural language arguments have been investigated.Some are likely to be reflected in linguistic features (e.g., an argument's arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability).In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument's text.In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions.Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity.We also find that human assessors differ more clearly to each other than to our approach.