Knowledge graphs have emerged as a widely adopted medium for storing relational data, making methods for automatically reasoning with them highly desirable. In this paper, we present a novel approach for inducing a hierarchy of subject clusters, building upon our earlier work done in taxonomy induction. Our method first constructs a tag hierarchy before assigning subjects to clusters on this hierarchy. We quantitatively demonstrate our method's ability to induce a coherent cluster hierarchy on three real-world datasets.

翻译：知识图表已成为一个广泛采用的存储关系数据的媒介,使得与其自动推理的方法非常可取。 在本文中,我们在先前在分类学上所做工作的基础上,提出了促使主题组分级的新办法。我们的方法首先构建一个标签级,然后将主题组分到这个等级组。我们量化地展示了我们的方法在三个现实世界数据集上引导一个连贯的分组级的能力。