Answering scholarly questions is challenging without the help of query-based systems. Thus, we develop a divide-and-conquer approach based on a Large Language Model (LLM) for scholarly Knowledge Graph (KG) Question Answering (QA). Our system integrates the KG ontology into the LLM prompts and leverages a hybrid prompt learning strategy with both query structure and content. Our experiments suggest that given an ontology of a specific KG, LLMs are capable of automatically choosing the corresponding classes or predicates required to generate a target SPARQL query from a natural language question. Our approach shows state-of-the-art results over one scholarly KGQA dataset, namely sciQA [1].