From Knowledge Graphs via Lego Bricks to scientific conversations.pptx

From Knowledge Graphs via Lego Bricks to scientific conversations - Dennis Madsen, Scientific Director, Novo Nordisk

1. From knowledge graphs via Lego bricks to scientific conversations Dennis Madsen Roland Hangelbroek Vinay Jethava NN GraphDay May 7, 2024

2. From knowledge graphs via Lego bricks to scientific conversations Dennis Madsen Roland Hangelbroek Vinay Jethava NN GraphDay May 7, 2024

3. Our knowledge graph principles Why Clear purpose with KG Relevant Pipelines to update Interoperability Entities and ontologies

4. Named Entity Recognition Entity Linking Relation Extraction NovoLinker

5. Named Entity Recognition

6. Entity Linking Ontology Vocabulary

7. Relation Extraction

8. Currently in NovoLinker - Entities Genes / Proteins – Linked to HGNC, Interpro and Protein Ontology Chemicals & Drugs – Linked to Chebi and NCIT Diseases & Phenotypes – Linked to MONDO and HPO Cell lines – Linked to cellosaurus Cell types – Linked to cell type ontology (CL) Geographical locations – Linked to Geonames Sequence features – Linked to sequence ontology Organizations – Linked to Wikidata , Crunchbase, ROR Organisms - Linked to NCBITaxon Anatomy – Linked to Uberon Gene ontology : Biological process Molecular function Cell component Others : Assays, medical procedures, devices, miRNA, sequence variants, persons

9. Currently in NovoLinker – Relations Protein – protein interactions Chemical – protein interactions Adverse drug effects Gene – disease relations Drug– drug interactions Causal relations

10. Use cases | Patents Long complex documents Rich relationships Patent Families CPC/IPCR classification hierarchy References (Patents/Articles) Inventors/Owners Additional information (figures, tables, sequences)

11. Use cases | Patents 11 Basic schema Text-based embeddings Node embeddings Entity detection using Novo Linker Enabling agentic interaction with the patent-graph

12. Patents (VNJE) a n a n si (RLHB) (Literature, n ews, c onference notes, p harma pipelines) N L Computational Biology (NYYL) N L Clinical Data (KEGS) NNRCO Screening Data (VMNZ) N L N L Lego bricks

13. Chat with data

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15. LLM Functions - API Patents (VNJE) a n a n si (RLHB) (Literature, n ews, c onference notes, p harma pipelines) N L Computational Biology (NYYL) N L Clinical Data (KEGS) NNRCO Screening Data (VMNZ) N L N L A P I

16. Use cases