Knowledge graphs and large language models for prompt-based scientometric inquiry

Scientometrics is undergoing a methodological transformation driven by the increasing availability of large-scale scientific data and advances in artificial intelligence (AI). Traditional approaches centered on citation analysis and bibliographic coupling are now complemented by methods that leverage semantic representations, structured knowledge, and natural language understanding. However, current generative AI systems pose inherent challenges such as hallucinations, lack of transparency in decision-making and explainability, and issues with source reliability. In this article, we seek to mitigate these challenges by outlining a framework that integrates knowledge graphs (KGs) and large language models (LLMs) for scientometric inquiry. Taking a socio-technical perspective, the article sets out to explore the intersectional space of computing and information science from a human-centered approach. The framework is designed to support both established scientometric tasks, such as trend analysis, topic detection, and collaboration mapping, and more exploratory, insight-generating applications, including question answering, knowledge discovery, and contextual enrichment of scientific content. Drawing on recent developments in the use of KGs and LLMs in scientific domains, we provide a comparative overview of existing work, identify key design principles, and discuss the advantages and limitations of such a framework. Our goal is to chart a pathway toward more interactive, transparent, and generative approaches in information science and cross-disciplinary research. • Multidimensional mapping from large bibliographic datasets remains challenging. • A KG-LLM framework is proposed to support interactive scientometric inquiry. • HITL validation addresses the black-box limitations of KG- and LLM-enabled solutions. • Implications for knowledge-based question answering are derived.