[51-98] -

by analyzing co-author networks and citation patterns. Link disparate profiles that belong to the same person.

As we move toward more AI-driven research, datasets like the enhanced MAKG will serve as the "brain" behind the next generation of scientific discovery. [51-98]

This blog post explores the findings and implications of the research article published in Quantitative Science Studies (2022), Volume 3, Issue 1, pages 51–98 . by analyzing co-author networks and citation patterns

Beyond knowing who wrote a paper, we need to know what it is about. The MAKG enhancement utilized machine learning to classify publications into a granular hierarchy of fields. This isn't just "Biology" vs. "Physics"; it's the ability to categorize niche sub-fields, making it easier for researchers to find relevant literature in a crowded digital landscape. 🧠 The Power of Embeddings This blog post explores the findings and implications

One of the most persistent headaches in bibliometrics is . If three different "J. Smith"s publish in physics, how do we know which one is the expert in quantum mechanics? The researchers introduced advanced algorithms to: