Novo Nordisk: When Knowledge Graphs meet LLMs
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When Knowledge Graphs meet LLMs - Keng Soh, Director, Intergrated OMICs and Tankred Hans Michael Ott, Advanced Data Scientist, Novo Nordisk
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Novo Nordisk: When Knowledge Graphs meet LLMs
- 1. Novo Nordisk® When KGs meet LLM Keng Soh Tankred Ott NN GraphDay May 7, 2024
- 2. When KGs meet LLMs GraphDay 7 May 2024 KEGS & THZO
- 3. Novo Nordisk® Overview 1. Why KGs with LLMs? 2. Demo 3. Roadmap
- 4. Novo Nordisk® Knowledge Graphs LLMs Pros: • Structural knowledge • Accuracy • Decisiveness • Interpretability • Domain-specific knowledge Pros: • Generalized “knowledge” • Superior language processing capabilities • Generalizability • Hallucination Cons: • Incomplete • Lacks language understanding • Unseen facts Cons: • Hallucination • Implicit knowledge • Black-box • Indecisiveness • Lack domain-specific knowledge Adapted from [2306.08302] Unifying Large Language Models and Knowledge Graphs: A Roadmap (arxiv.org)
- 5. Novo Nordisk® Business value impact 1. Improve data search efficiencies 2. Systematize domain knowledge capture from experts 3. Harness user thought processes, i.e. expert and institutional knowhow, to refine agents 4. Accelerate data to knowledge to insight cycle for users
- 6. Novo Nordisk® Workflow Files Databases Clinical data • Clinical MDR • BioPages • GiaNNt • COSMOS • BioHome • SDTM/ADaM • Clinical Omics Gold DB LLM & UI
- 7. Novo Nordisk® Biomedical Knowledge Graph etc 14 public data sources and more …
- 8. Novo Nordisk® Drug Disease Protein Pathway Protein Pathway Protein Mechanism of Action of Drug to Disease LLM/Graph- based predicted link treats targets downregulates ameliorates ameliorates downregulates downregulates downregulates targets
- 10. Novo Nordisk® Use Case: Clinical omics data for treatment signatures and MoA for drug repurposing Source: DrugMechDB Example of a metapath showing the mechanism of action of liraglutide
- 11. Novo Nordisk® Modular KGs with KG-aware agentic LLMs Query_Planner Research_Assistant User_Proxy Summary_writer Reviewer KG_Assistant Brainstorm_Expert KG_Planner KG Tools uses • Get a list of nodes from a description or name • Count number of nodes • Get k-shortest paths between a drug and disease node • Get a list of all possible node types • Get neighbors of a root node
- 14. Novo Nordisk® Workflow Files Databases Clinical data • Clinical MDR • BioPages • GiaNNt • COSMOS • BioHome • SDTM/ADaM • Clinical Omics Gold DB RAG workflow etc LLM & UI
- 15. Novo Nordisk® Working towards fully connected end2end schema Protocol CDISC Controlled Terminology NN ontologies SDTM ADaM Omics Imaging Target validation Digital Biomarkers Signatures Targets Biomarker development Drug repurposing Endpoints New trials NN KG schema RWD
- 16. Novo Nordisk® Knowledge Graphs LLMs Adapted from [2306.08302] Unifying Large Language Models and Knowledge Graphs: A Roadmap (arxiv.org) Agents Common semantic layer Communication
Editor's Notes
- THZO
- In this presentation, we are going to explore how Knowledge Graphs and Language Model Models (LLMs) can work together to unlock the true potential of artificial intelligence and machine learning.
- We will begin by exploring the basics of Knowledge Graphs and LLMs, discussing the relationship between the two and how they can be combined to create more powerful and accurate models. Then, we will dive into the many benefits of using Knowledge Graphs to integrate with LLMs, highlighting several real-world use cases. Finally, we will explore practical considerations and best practices for implementing this powerful combination of technologies.