In this chapter, we focus on the role of identity links in knowledge-based explainable systems, i.e. systems that rely on background knowledge from knowledge graphs to build explanations. With the rise of explainable transparent methods in the area of eXplainable AI, systems integrating multiple sources of aligned knowledge will become more and more common. We hypothesize that the interpretability and results of these systems could be affected by the discrepancy and misalignment between knowledge sources – a widely known problem in the Knowledge Representation community. We therefore study the role of identity in knowledge-based explainable systems, i.e. if and how explainable systems do rely on multiple knowledge graphs, then show examples of the impact of misusing identity on the interpretability of a system. Finally, we describe methods that can promote the correct alignment of knowledge sources. Our hope is to provide support to improving current knowledge-based explainable methods and, more in general, foster a better integration of knowledge representation and explainable AI.