EXplainable Artificial Intelligence (XAI) has recently become a very active domain, mainly due to the extensive development of black-box models such as neural networks. Recent XAI objectives have been defined in the state-of-the-art, for which specific approaches have been proposed. Implicit links can be found between XAI and other domains, especially related to knowledge and neural networks. We here aim to highlight these implicit links. We present a narrative review of research works in two domains: (i) Knowledge domain with focus on Knowledge Discovery and Representation, and (ii) Representation Learning. We discuss the similarity and joining points between these domains and XAI. We conclude that, in order to make black-boxes more transparent, XAI approaches should be more inspired and take advantage of past and recent works in Knowledge and Representation Learning domains. Through this paper, we offer an entry point to the domain of XAI for both multidisciplinary researchers and specialists in AI, as well for AI knowledgeable users.