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- 1. Graph Summit |London | February 22nd, 2024 Knowledge Graphs powering a fast- moving global life sciences organisation Our experience building a knowledge graph platform and service to power the next generation of insights and analytics at AstraZeneca Varun Bhandary Senior Solutions Architect Enterprise Data & AI Architecture IGNITE (AZ) Antonio Fabregat, PhD Knowledge Graph Lead Enterprise Data Office IGNITE (AZ)
- 2. Agenda 1. Connected Dataโค๏ธ Lifesciences 2. Our Challenges and Plan ๐ 3. Introducing AZโs โKnowledge Graph Service" ๐ฃ 4. A glimpse into the future of Graphs in AZ ๐ญ 5. Talking to your Graphs ๐ฃ๏ธ๐๏ธ 6. Graphs are Stronger Together ๏ธ 2
- 3. AstraZeneca in UK 3 Reference: https://www.astrazeneca.co.uk/about-us/economic
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- 6. Why Knowledge Graphsat Lifesciences? 6 Integration of Diverse Data Sources A unified framework for connecting heterogeneous data, enabling researchers and decision-makers to gain comprehensive insights across disparate data silos. Complexity of Biomedical Knowledge Facilitate advanced analytics, hypothesis generation, and decision support for drug discovery, development, and clinical research. Semantic Search and Discovery Enable semantic search and discovery by encoding relationships between entities, concepts, and attributes in a graph-based data model Data-driven Insights and Decision Making A powerful foundation for advanced analytics, machine learning etc enabling researchers to uncover hidden patterns
- 7. Use-Cases 7 Drug Discovery Regulatory Affairs PatientStudy Compounds CRM (Engagement & Reach) Competitive Insights Supply Chain Quality Planning Real World Evidence Many moreโฆ.
- 8. Knowledge Graphs representationalternatives 8 * Adapted from documentation at W3C https://www.w3.org/ Two ways of representing/storing a Knowledge Graph RDF-star (Resource Description Framework) Semantic Web: Good for common standards and data exchange Data model based on 3 parts: subject, predicate and objects Nodesโ properties added as predicates. Edges with properties are โtriple-resourcesโ (like โmeta-nodesโ) Storage: โTriple/Quad Storesโ Graph Databases Any type of real-world information, can be represented in a Knowledge Graph 18 nodes (5 instances, 4 classes, 8 literals, 1 triple-resource) 19 relationships (triples) Knowledge Graph is a way of organizing data & information in the form of a graph A collection of interlinked concepts, entities, events that represent a network of real-world entities, the relationships between them. LPG (Labelled-Property Graph) Good for highly dynamic, transactional use cases Data organized as nodes, labels, relationships and properties Both nodes and edges can have properties Storage: Native Graph Databases 5 nodes (5 ids, 4 Labels, 8 properties) 4 relationships (2 properties)
- 9. Our Challenges and Plan๐ 2
- 10. Challenges 10 Decouple & SpecialiseIntegrate & Standardise Abstract & Automate ๏ผ Use the right tools for the job Data Lake? Data Warehouse? Graph Database? LPG? RDF? No-SQL? ๏ผ Modular Design with Security in Mind Build a component-based architecture with coherent and practical principles. ๏ผ Think of data as a product Push and Pull Vs Serve and Consumer ๏ผ Make it easy to work with data across platforms. Searching and moving data is costly. Move to an ELT model, leverage first-party connectors, and document to promote the most optimal options. ๏ผ Standardise Apply FAIR principles ๏ผ Document and Promote Patterns Data Movement, Loading, Transformation. ๏ผ Template and Accelerate Teams should be able to spend more time analysing data and deriving insights than managing infra. ๏ผ Automate Leverage IaC, and automation pipelines to achieve consistent deployments.
- 11. The Plan Data Platform UnifiedData Store Snowflake External Tables Snowflake Internal Table Storage Unified Data Compute Snowflake Virtual Warehouse Snowflake Snowpark SnowPipe User Defined Functions Unistore Time-Travel Data-Lake Compute SQL Cluster General Purpose Cluster Data Lake Store Raw Layer Work Layer Publish Layer Glue Hive Metastore Knowledge Graph Service Graph Data Store LPG Storage Composite Utilities Graph DS Libraries Cypher / APOC Graph Compute Graph Build and Exploration Graph Analytics Machine Learning Studio Model Build & Train Deploy and Govern Graph Exploration Query Client Data Browser Graph Data Visualization External Data, RWE & Partnerships Structured Data MDM/RDM, Ontologies, Vocab., Dictionaries Semi-Structured Content & Files Un-Structured Content & Files User Input Data Acquisition Data Sources Ingestion & Integration IoT & Streaming API Management Event Store Queue MuleSoft CDC Database API Streaming Compute External Data Transfer DDTS Enterprise Platforms (i.e. SAP) Decreasing Volume of Content Increasing Quality of Content
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- 13. Why Knowledge Graphs?and why a Service? 13 โข Data management and analysis โข Overcoming data silos and integration challenges Growing importance of knowledge graphs โข Hosting and development support for knowledge graphs โข Robust and scalable solutions โข Enhanced data-driven decision-making Need for efficient and reliable services โข Improved data accessibility and insights โข Streamlined collaboration and innovation Benefits for businesses and organizations
- 14. 14 Why using the Knowledge GraphService?
- 15. 15 Why using the Knowledge GraphService?
- 16. 16 Why using the Knowledge GraphService?
- 17. A Glimpse into theFuture of Graphs at AZ ๐ญ 4
- 18. Biology | MarketStrategy | Logistics | Environmental targets 18 Biological Insights Knowledge Graph Graph machine learning to help scientists make faster & better drug discovery decisions Competitive Intelligence Knowledge Graph One-stop-shop for competitive intelligence, transforming a manual system into a rich service Supply Chain Knowledge Graph Insights into the companyโs supply chain, streamlining processes to enhance decision-making Sustainability Initiative Decision-making support system aiming to reduce the companyโs carbon footprint
- 19. Compounds 19 Compounds Synthesis & Management (CSMKG) Combineseveral databases Transforms operational data into business insights to drive continuous improvements in storage, logistics and delivery High Throughput Screening (HTSKG) Contains ยฃM worth of data Increases the quality and efficiency of future HTS screens Compounds & Fragments (CFKG) Creates a view of the chemical space like a medicinal or computation chemist. Contains all internal and selected external libraries and allows users to modify a search and receive feedback โliveโ
- 20. PharmaSci 20 Formulation Knowledge Graph Pre-clinical formulationdesign process Leading to quicker, more effective scientific developments Boston Formulation Knowledge Graph Improves the understanding of our data Enhances collaboration by breaking down silos and connecting disparate data sources Lipid Nano Particles Knowledge Graph Machine learning models Predicts in-vivo activity from in-vitro data for intra-cellular drug delivery and LNP formulation design
- 21. Talking to yourgraphs ๐ฃ๏ธ๐๏ธ 5 Have you ever thought to have a graph expert with you 24/7? GenAI is here to help!
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- 23. Future Evolutions ofthe Insights Chat Knowledge Mesh? 23 Unified Rule, Behavior & Meta Graph Store User User Knowledge Discovery Interface Unified LLM Integration (AI Portal) 1 2 3 Domain Specific Knowledge Graphs Domain Specific Knowledge Graphs Meta Graph Meta Graph Meta Graph
- 24. Graphs are Stronger Together๏ธ Why query federation is a key to unlocking even more cross-functional use-cases 6
- 25. Siloed data lookslikeโฆ 25
- 26. 26 Letโs build bridgesto connect โsiloesโ of interestโฆ Query federation describes a collection of features that enable users and systems to run queries against multiple siloed data sources without needing to migrate all data to a unified system. Federated Queries are these BRIDGES
- 27. 27 Letโs build bridgesto connect โsiloesโ of interestโฆ The diagram shows the resulting subgraph for the federated query that answers the question โFind all genes in BIKG linked with a specific disease, and then all trials in CIKG that are testing drugs targeting those genesโ Biological Insights Knowledge Graph Competitive Intelligence Knowledge Graph CIKG
- 28. Acknowledgments โข Aaron Holt โขNicolas Mervaillie โข Joe Depeau โข Job Maelane โข Yuen Leung Tang โข Jesus Barrasa โข Morgan Senechal โข Lauren Eardley โข Cinthia Willaman โข Taylan Sahin โข Melanie Hardiman โข Daniel Addison โข Delyan Ivanov โข Suzy Jones โข Andriy Nikolov โข Cristina Mihetiu โข Michaรซl Ughetto โข Karen Roberts โข Wolfgang Klute โข Michael Lainchbury โข Justin Morley โข Andy Stafford-Hughes โข Nikil Kunnappallil โข Anthony Puleo โข Ivan Figueroa โข Koushik Srinivasan โข Nick Iles โข Lena Becciolini
- 29. Enterprise Data Office| IGNITE Enterprise Knowledge Graph Robert Hernandez Knowledge Engineering Lead Sandra Carrasco Senior Knowledge Graph Engineer Antonio Fabregat Knowledge Graph Lead Vishal Kumar DevOps & Data Engineer Preetha Mutharasu Knowledge Graph Engineer Ronnie Mubayiwa Senior DevOps Engineer Varun Bhandary Senior Solution Architect Sree Balasubramanyam Senior IT Project Manager Prem Oliver Vincent Scrum Master Sangeetha Natarajan Testing Manager Miquel Monge Knowledge Graph Engineer Pascual Lorente Senior Knowledge Graph Engineer Santanu Biswas Senior Datalake Engineer Tarik Sidi-Mammar Data Ops Platforms Service Lead Lauren Eardley Enterprise Head of Data Engineering Services