Relation Extraction

A graduate research project at UIUC (CS 546, Fall 2024) proposing SHERPA — a semi-structured non-parametric memory framework that integrates a knowledge graph with a hierarchical vector store to improve retrieval-augmented generation. Five-person team; this page covers my two contributions: Query Classification and Entity-Relation Extraction. The interesting story isn’t the destination — it’s the experimental funnel. Both contributions involved walking through multiple wrong-fits before landing on the architecture that shipped. ...

Context At NCSA, I worked on improving reasoning and retrieval in biomedical AI systems by incorporating knowledge graphs into LLM workflows. The research focused on building a structured knowledge base of genes, phenotypes, and biomedical relationships to support causal question-answering over CRISPR perturbation data. What I Built I built a Neo4j knowledge graph with 100K+ biomedical triples by harmonizing Monarch ontology data, creating a structured layer for multi-hop reasoning over biomedical entities. ...