Knowledge graphs (KGs) promise machine-readable semantics together with human-readable labels and relations, yet domain experts struggle to use them due to technical barriers, especially in automatically constructed graphs with unlabeled nodes and complex connections. While LLMs enable KG interfaces such as retrieval-augmented generation, their potential to leverage ontology definitions to improve graph readability remains unexplored. This research investigates LLMs as ontology-aware verbalization interfaces that generate human-readable and usable textual information encoded in the KG structure.