Embeddings + topic modeling to match researchers and papers by interest/expertise — accelerates collaboration.
Problem
Researchers struggle to find relevant papers and collaborators, slowing scientific progress. Current tools rely heavily on keyword search and miss deeper similarities in research focus. Semantic connections between research areas are often invisible, making it difficult to discover interdisciplinary opportunities or find collaborators working on related problems from different angles.
Overview
This project creates a semantic matchmaking tool that connects researchers with relevant papers and collaborators using embeddings and topic modeling. The system understands research content at a deeper level than keyword matching, identifying conceptual similarities even when different terminology is used. It helps researchers discover connections they might otherwise miss.
How It Works (Approach)
The system processes research abstracts, full papers, and researcher profiles from ResearchGate and other academic databases. It generates semantic embeddings using transformer models trained on scientific text, then clusters research by topic using unsupervised learning. A similarity search engine (powered by FAISS) identifies the most relevant papers and researchers for each query. The system also uses retrieval-augmented generation (RAG) to provide context-aware recommendations and explanations for why matches are relevant.
Impact / Value
Researchers spend less time searching and more time creating, accelerating the pace of scientific discovery. The system supports collaboration by connecting researchers with complementary expertise, enables discovery of interdisciplinary research opportunities, and helps early-career researchers find mentors and collaborators. By surfacing semantic connections, the tool facilitates knowledge transfer and innovation across research domains.
Key Features
- Embedding-based researcher and paper matching
- Topic modeling for research themes
- Similarity search for collaboration discovery
- RAG-supported recommendations
- Interactive exploration interface