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We present a comprehensive benchmark dataset for Knowledge Graph QuestionAnswering in Materials Science (KGQA4MAT), with a focus on metal-organicframeworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) hasbeen constructed by integrating structured databases and knowledge extractedfrom the literature. To enhance MOF-KG accessibility for domain experts, we aimto develop a natural language interface for querying the knowledge graph. Wehave developed a benchmark comprised of 161 complex questions involvingcomparison, aggregation, and complicated graph structures. Each question isrephrased in three additional variations, resulting in 644 questions and 161 KGqueries. To evaluate the benchmark, we have developed a systematic approach forutilizing the LLM, ChatGPT, to translate natural language questions into formalKG queries. We also apply the approach to the well-known QALD-9 dataset,demonstrating ChatGPT's potential in addressing KGQA issues for differentplatforms and query languages. The benchmark and the proposed approach aim tostimulate further research and development of user-friendly and efficientinterfaces for querying domain-specific materials science knowledge graphs,thereby accelerating the discovery of novel materials.

Knowledge Graph Question Answering for Materials Science (KGQA4MAT):  Developing Natural Language Interface for Metal-Organic Frameworks Knowledge  Graph (MOF-KG) Using LLM