The rise of large language models (LLMs) has advanced information retrieval, yet issues like limited knowledge updating, lack of transparency and interpretability, as well as hallucinations persist. Retrieval-augmented generation (RAG) addresses these problems, though it still lacks interpretability due to reliance on opaque vector-based representations. Our work presents a RAG framework using a knowledge graph (KG) as the primary knowledge base to address this problem, relying solely on open-source components to enable user customization. Our pipeline comprises multiple stages: (i) a translation module for multilingual support, (ii) entity linking, (iii) knowledge retrieval through verbalized triples or SPARQL query generation, and (iv) answer generation, which incorporates ontology (properties and classes) retrieval. We evaluate our system on Wikidata, DBpedia, and a domain-specific KG. With the optimal configuration determined through an ablation study, the system achieves Jaccard similarity scores of 0.458, 0.517, and 0.976 for each respective KG. The ablation study further reveals that ontology retrieval is the most crucial component in providing context to the LLM in generating SPARQL queries. © 2025 Copyright for this paper by its authors.