Software repositories contain valuable information for gaining insights into their development process. However, extracting insights from these repository data is time-consuming and requires technical expertise. While software engineering chatbots have been developed to facilitate natural language interactions with repositories, they struggle with understanding natural language and accurately retrieving relevant data. This study aims to improve the accuracy of LLM-based chatbots in answering repository-related questions by augmenting them with knowledge graphs. We achieve this in a two-step approach; (1) constructing a knowledge graph from the repository data and (2) synergizing the knowledge graph with LLM to allow for the natural language questions and answers. We curated a set of 20 questions with different complexities and evaluated our approach on five popular open-source projects. Our approach achieved an accuracy of 65%. We further investigated the limitations and identified six key issues, with the majority relating to the reasoning capability of the LLM. We experimented with a few-shot chain-of-thought prompting to determine if it could enhance our approach. This technique improved the overall accuracy to 84%. Our findings demonstrate the synergy between LLMs and knowledge graphs as a viable solution for making repository data accessible to both technical and non-technical stakeholders.

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