Knowledge bases are widely used for information management, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g., for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify how fair the decisions are, a number of explanation approaches have been proposed. An intrinsically transparent way to do classification is by using concepts in description logics. However, these concepts can become long and difficult to fathom for non-experts, even when verbalized. One solution is to employ counterfactuals to answer the question, ``How must feature values be changed to obtain a different classification?'' By focusing on the minimal feature changes, the explanations are short, human-friendly, and provide a clear path of action regarding the change in prediction. While previous work investigated counterfactuals for tabular data, in this paper, we transfer the notion of counterfactuals to knowledge bases and the description logic $\mathcal{ELH}$. Our approach starts by generating counterfactual candidates from concepts, followed by selecting the candidates requiring the fewest feature changes as counterfactuals. When multiple counterfactuals exist, we rank them based on the likeliness of their feature combinations. We evaluate our method by conducting a user survey to determine which counterfactual candidates participants prefer for explanation.

翻译：知识库被广泛用于信息管理，支撑着网络搜索、问答系统和自然语言处理等高影响力应用。它们也是自动决策系统的核心基础，例如医疗诊断和信用评分。由于受这些决策影响的利益相关者希望了解自身处境并验证决策的公平性，已有多种解释方法被提出。一种内在透明的分类方式是使用描述逻辑中的概念。然而，即便经过语言化表述，这些概念对非专业人士而言仍可能变得冗长且难以理解。一种解决方案是利用反事实来回答这一问题："特征值必须如何改变才能获得不同的分类结果？"通过聚焦于最少的特征变化，解释变得简洁、人性化，并为预测结果的改变提供了清晰的行动路径。尽管先前的研究针对表格数据探讨了反事实，本文则将反事实的概念迁移至知识库和描述逻辑 $\mathcal{ELH}$。我们的方法首先从概念中生成反事实候选，随后选取需要最少特征变化的候选作为反事实。在存在多个反事实的情况下，我们根据其特征组合的合理性进行排序。我们通过开展用户调查来评估该方法，以确定参与者更偏好哪些反事实候选作为解释。