In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our interpretable algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is exponentially large given long and complicated text data, we design a learnable anchor algorithm to better extract local and domain knowledge-oriented explanations. A set of regulations is further introduced, combining learned interpretable representations with anchors and information extraction to generate comprehensible semantic explanations. To carry out an extensive experiment, we first develop a drug abuse ontology (DAO) on a drug abuse dataset on the Twittersphere, and a consumer complaint ontology (ConsO) on a consumer complaint dataset, especially for interpretable ML. Our experimental results show that our approach generates more precise and more insightful explanations compared with a variety of baseline approaches.

在本文中，我们介绍了一种新颖的解释框架，该框架基于基于本体的采样技术学习可解释模型，以解释不可知的预测模型。与现有方法不同，我们的可解释算法考虑了领域知识本体中描述的单词之间的上下文相关性，以生成语义解释。为了缩小解释的搜索空间，在给定长而复杂的文本数据的情况下，解释的搜索空间呈指数级增长，我们设计了一种可学习的锚算法，以更好地提取本地和领域知识导向的解释。进一步引入了一组规则，将学习到的可解释表示与锚点和信息提取相结合，以生成可理解的语义解释。为了进行广泛的实验，我们首先在 Twittersphere 上的药物滥用数据集上开发药物滥用本体（DAO），并在消费者投诉数据集上开发消费者投诉本体（ConsO），特别是对于可解释的 ML。我们的实验结果表明，与各种基线方法相比，我们的方法产生了更精确和更有洞察力的解释。