The integration of Knowledge Graphs (KGs) into the Retrieval Augmentation Generation (RAG) framework has attracted significant interest, with early studies showing promise in mitigating hallucinations and improving model accuracy. However, a systematic understanding and comparative analysis of the rapidly emerging KG-RAG methods are still lacking. This paper seeks to lay the foundation for systematically answering the questions of when and how to use KG-RAG by analyzing its performance in various application scenarios associated with different technical configurations. After outlining the mind map using the KG-RAG framework and summarizing its popular pipeline, we conduct a pilot empirical study of KG-RAG works to reimplement and evaluate 6 KG-RAG methods across 9 datasets in diverse domains and scenarios, analyzing the impact of 9 KG-RAG configurations in combination with 17 LLMs. Our results underscore the critical role of appropriate application conditions and optimal configurations of KG-RAG components. The full appendix, data, and methods used in our paper, along with our reimplementation, are available on https://github.com/XujieYuan/Understanding-KG-RAG .