Open Research Knowledge Graph (ORKG) reposted this
🚀 "𝗧𝗮𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗪𝗶𝗹𝗱 𝗪𝗲𝘀𝘁: 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀 𝗶𝗻 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗟𝗶𝗯𝗿𝗮𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀" 👉 Just published in the 𝗖𝗼𝗱𝗲𝟰𝗟𝗶𝗯 𝗝𝗼𝘂𝗿𝗻𝗮𝗹 (𝗜𝘀𝘀𝘂𝗲 𝟲𝟬): https://lnkd.in/eHTmA-GD 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: 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 #MetaAI'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 central project of the TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek, this approach transforms scientific documentation into machine-actionable knowledge, streamlining research access, update, search, and comparison. 𝘁𝗹;𝗱𝗿: I explore how 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗹𝗶𝗸𝗲 𝗟𝗟𝗠𝘀 can be represented with the help of 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀—enabling machine-actionable, FAIR-aligned descriptions that go beyond PDFs and keyword search. 🧠 Using the Open Research Knowledge Graph (ORKG), I show how we can structure research as dynamic, comparable, and queryable knowledge—supporting FAIR practices for science communication. 💡 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • A vision for machine-actionable digital libraries • A structured catalog of 90+ LLMs (e.g., GPT-4, LLaMA, Claude) • Visualizations, comparisons, reviews, and SPARQL queries 📚 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: • #ORKG Catalog of Transformer Models: https://lnkd.in/e3u-2t-Y • Google DeepMind's LLMs Comparison: https://lnkd.in/eACB5cwe • OpenAI's Early GPT Series: https://lnkd.in/ec2vsQnJ • #ORKG Review: https://lnkd.in/eWCAAspM • GitHub: https://lnkd.in/ewRifxZV Grateful to the #Code4Lib community for the opportunity to share this work! #KnowledgeGraphs #DigitalLibraries #FAIRData #GenerativeAI #LLMs #OpenScience #SemanticWeb
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