Knowledge Graphs (KG) have become a widespread knowledge representation. When different KGs exist for some domain, it is valuable to merge them into a richer KG. This is known as the problem of KG alignement, which encompasses related problems such as entity alignement or ontology matching. Although most recent approaches rely on supervised representation learning, Formal Concept Analysis (FCA) has also been proposed as a basis for symbolic and unsupervised approaches. We here explore the application of Graph-FCA, an extension of FCA for KGs, to different scenarios of KG alignments: (A) when the two KGs have common values, and (B) when pre-aligned pairs are known. We show that, compared to previous FCA-based approaches, Graph-FCA allows for a more natural and scalable representation of the KGs to be aligned, and makes it simpler to extract alignments from the concepts. It also features flexibility w.r.t. different alignment scenarios.