Ontology-Backed GraphRAG: Injecting Biomedical Logic in LLMs for Drug Discovery
summary
Claims an ontology-backed GraphRAG system cuts LLM hallucinations and raises precision for target identification, drug repurposing and mechanism‑of‑action prediction by feeding a formal ontology into Neo4j and running a Cypher-generating LLM pipeline. Implementation uses Cypher templates, dynamic and text‑to‑Cypher generation, a node index for fuzzy matching, and a downstream LLM summarizer. Prototype returns accurate subgraphs but requires ontology subsetting, result limits and careful token management to avoid mis-typing, oversized queries, and partial answers. Overall it works with operational tradeoffs.
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chapters (11)
- 0:00Introduction to GraphRAG
- 1:56GraphRAG implementation
- 3:10The role of ontologies
- 4:18Building the graph model
- 6:41Refining graph navigation
- 7:27System architecture flow
- 8:32Optimizing search accuracy
- 10:08Managing schema complexity
- 11:12System constraints and limits
- 12:18Balancing LLMs with logic
- 13:07Conclusion and future outlook
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