SRME

SRME (Semantic Research Matchmaking Engine) was built to solve the "discovery gap" in academic institutions. By mapping the semantic distance between research papers and faculty expertise, it provides a data-driven way to find collaborators and mentors.

Data Pipeline

The engine orchestrates a complex pipeline that scrapes metadata from 500+ faculty profiles, extracts semantic meaning using state-of-the-art NLP models (BERT/Sentence-Transformers), and stores them in a vector database for millisecond-latency retrieval.

Semantic Intelligence

Traditional keyword search fails to capture the nuances of interdisciplinary research. SRME uses Vector Similarity (Cosine) to identify hidden connections between researchers who might use different terminology for similar concepts.

Visualization

Research connections are surfaced through an interactive knowledge graph, allowing users to visually navigate the "expertise clusters" within their organization.