Knowledge bases are now first-class citizens of the Web. Circa 50% of the 3.2 billion websites in the 2022 crawl of Web Data Commons contains knowledge base fragments in RDF. The 82 billion assertions known to exist in these websites are complemented by a roughly comparable number of triples available in dumps. As this data is now the backbone of a number of applications, it stands to reason that machine learning approaches able to exploit the explicit semantics exposed by RDF knowledge bases must scale to large knowledge bases. In this chapter, we present approaches based on continuous and symbolic representations that aim to achieve this goal by addressing some of the main scalability bottlenecks of existing class expression learning approaches. While we focus on the description logic ALC , the approaches we present are far from being limited to this particular expressiveness.