Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.

中文翻译：

---

使用实体和词嵌入的大规模分类法归纳

分类法是知识组织的重要组成部分，并充当智能系统（例如形式本体）中更复杂的知识表示的基础。但是，手动建立分类法是一项昂贵的工作，因此，自动进行分类法的方法是构建大规模分类法的一个不错的选择。在本文中，我们提出了TIEmb，这是一种使用实体和文本嵌入从知识库中自动提取非监督类包含公理的方法。我们将这种方法应用于WebIsA数据库中，该数据库是从万维网的大部分中提取的包含关系的数据库，用于提取Person and Place域中的类层次结构。