With the ever-growing adoption of Artificial Inteligence (AI) models comes an increasing demand for making their output actions understandable.With this aim, it is crucial to generate natural language explanations of their models. One way of achieving this goal is to translate the languages of the Semantic Web (SW) into natural language. In this chapter, we give an overview of how SW languages can be used to generate texts and consequently explanations. We begin by presenting LD2NL, a framework for verbalizing the three key languages of the Semantic Web, i.e., RDF, OWL, and SPARQL. Afterward, we talk about the generation of texts by relying on Neural Network (NN) models. We hence present NeuralREG, an approach for generating referring expression of Knowledge Graph (KG) entities while generating texts. Both frameworks are evaluated in open surveys with 150 persons. The results suggest that although generating explanations from KGs is in its infancy, both LD2NL and NeuralREG can generate verbalizations that are close to natural languages, and non-experts can easily understand that. In addition to that, it enables non-domain experts to interpret AI actions with more than 91% of the accuracy of domain experts.