EvoLearner：用进化算法学习描述逻辑
EvoLearner: Learning Description Logics with Evolutionary Algorithms
摘要：
对知识图谱中的节点进行分类是一项重要的任务，例如，用于预测缺失的实体类型，预测哪些分子导致癌症，或预测哪些药物是有希望的治疗候选物。虽然黑盒模型通常具有很高的预测性能，但它们只是事后和局部可解释的，并且不允许学习的模型容易地用领域知识来丰富。为此，学习描述逻辑概念，从正面和负面的例子已经提出。然而，学习这些概念通常需要很长时间，并且最先进的方法对文字数据值的支持有限，尽管它们对许多应用程序至关重要。在本文中，我们提出了EvoLearner -一种进化方法来学习ALCQ（D）中的概念，ALCQ（D）是带有补充的属性语言（ALC），带有合格的基数限制（Q）和数据属性（D）。我们贡献了一个新的初始化方法的初始人口：从积极的例子，我们执行有偏随机游动，并将其转换为描述逻辑概念。此外，我们通过在决定在哪里分割数据时最大化信息增益来提高对数据属性的支持。我们证明了我们的方法在结构化机器学习的基准测试框架SML-Bench上的表现明显优于最先进的水平。我们的消融研究证实，这是由于我们的新的初始化方法和数据属性的支持。
英文摘要：
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 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, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
