MAKES-QA: A multi-agent framework for knowledge graph construction and enrichment over scientific literature for question answering

• Proposes MAKES-QA, a modular multi-agent framework for scientific KGQA. • Enables dynamic KG construction and enrichment using LLMs. • Supports user-guided graph expansion via three enrichment strategies. • Combines KG-based retrieval with LLMs for accurate QA. Knowledge Graphs (KGs) offer an effective framework for organizing complex information, yet their construction and use for question answering face significant challenges due to the dynamic and evolving nature of research domains. In this work, we present MAKES-QA , a modular and dynamic multi-agent framework for KG-based question answering (KGQA) that leverages Large Language Models (LLMs) to extract, normalize, and integrate knowledge, particularly in the context of scientific literature. The framework is explicitly designed as a research support tool for domain-aware users, such as researchers and practitioners, enabling guided and informed interaction during knowledge graph construction and exploration. The system enables iterative enrichment of the KG through user-guided strategies, facilitating the discovery and integration of new information. Once constructed, the KG supports natural language queries via a Retrieval-Augmented Generation (RAG) approach that combines semantic retrieval of relevant triples with LLM-based answer synthesis. This architecture ensures accurate, context-aware responses grounded in curated scientific knowledge. Our approach promotes efficient exploration of scientific domains, reducing the need for exhaustive manual reading while enabling flexible knowledge discovery. The MAKES-QA source code is available at https://github.com/MODAL-UNINA/MAKES-QA .