Taming the Generative AI Wild West: Integrating Knowledge Graphs in Digital Library Systems

Since the 17th century, scientific publishing has been document-centric, leaving knowledge—such as methods and best practices—largely unstructured and not easily machine-interpretable, despite digital availability. Traditional practices reduce content to keyword indexes, masking richer insights. Advances in semantic technologies, like knowledge graphs, can enhance the structure of scientific records, addressing challenges in a research landscape where millions of contributions are published annually, often as pseudo-digitized PDFs. As a case in point, generative AI Large Language Models (LLMs) like OpenAI's GPT and Meta AI's LLAMA exemplify rapid innovation, yet critical information about LLMs remains scattered across articles, blogs, and code repositories. This highlights the need for knowledge-graph-based publishing to make scientific knowledge truly FAIR (Findable, Accessible, Interoperable, Reusable). This article explores semantic publishing workflows, enabling structured descriptions and comparisons of LLMs that support automated research insights—similar to product descriptions on e-commerce platforms. Demonstrated via the Open Research Knowledge Graph (ORKG) platform, a flagship project of the TIB Leibniz Information Centre for Science & Technology and University Library, this approach transforms scientific documentation into machine-actionable knowledge, streamlining research access, update, search, and comparison.