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- 1. GraphSummit | London| November 14th, 2023 Empowering AZ’s Data Connectivity Building an Internal Knowledge Graph Service to foster Knowledge Graph projects, enhancing Data Reusability with Federated Queries, and harvesting LLM power to talk to the Graphs Antonio Fabregat, PhD Knowledge Graph Lead Enterprise Data Office, IGNITE (AZ)
- 2. Presentation Outline • Revolutionizing DataManagement • Internal deployment of the Graph as a Service capability • A view of the current AZ´s Knowledge Graph project landscape • Queries Federation across several Knowledge Graphs • Harvesting LLM power to talk to the graphs 2
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- 4. Tackling Data Growthwith Knowledge Graphs 4 • Drug Discovery and Research Data • Genomic and proteomic data, high-throughput screening results, clinical trial data, etc. • Real-World Evidence and Patient Data • Electronic health records (EHRs), wearable devices and remote monitoring, patient-reported outcomes, etc. • And more… Rapid data growth from various sources • Data management • Analysis tools Increasing need for efficient • Organize and structure data • Facilitate easier access and analysis Knowledge Graphs as a solution
- 5. Knowledge Graphs vsTraditional Data Management Systems 5 •Struggle with complex relationships and semantics •Limited in capturing meaning and context Traditional data management systems •Excel at representing relationships and semantics •Understand meaning and context of data •Enable more effective analysis and insights Knowledge graphs advantages
- 6. AI and MachineLearning with Knowledge Graphs 6 Increasing use of AI and machine learning in Data Analysis •Need for efficient data representation and processing Knowledge Graphs as a Solution •Enriched data model •Enhance AI and machine learning algorithms' understanding •Improve data analysis capabilities
- 7. Data Integration andInterconnectivity 7 Seamless integration of data from various sources • Creation of a unified view of information Overcoming Data Silos •Interconnectivity of Knowledge Graphs Increased value from Data Assets •Enhanced Data Analysis and Insights •Improved Decision-Making
- 8. Enhanced Search andDiscovery 8 Improved Search and Discovery Capabilities More intuitive Query Languages Uncovering hidden relationships within data Accurate and Relevant Search Results Enhanced user experience Increased productivity
- 9. Personalisation and Recommendation 9 •Rich data representation and relationship mapping Understanding user preferences, behaviour, and context • Relevant content based on user interests Personalized and targeted recommendations • Improved customer satisfaction and retention Increased user engagement
- 10. Knowledge Graphs representationalternatives 10 * 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 Nodes’ properties added as predicates. Edges with properties are “triple-resources” (like “meta-nodes”) Storage: “Triple/Quad Stores” 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)
- 11. Internal Graph as aService capability 11
- 12. Why Knowledge Graphs?and why a Service? 12 • Data management and analysis • Overcoming data silos and integration challenges Growing importance of knowledge graphs • Hosting and development support for knowledge graphs • Robust and scalable solutions • Enhanced data-driven decision-making Need for efficient and reliable services • Improved data accessibility and insights • Streamlined collaboration and innovation Benefits for businesses and organizations
- 13. A view ofthe AZ´s Knowledge Graph Project Landscape 13
- 14. Biology | MarketStrategy | Logistics | Environmental targets 14 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 company’s supply chain, streamlining processes to enhance decision-making Sustainability Initiative Decision-making support system aiming to reduce the company’s carbon footprint
- 15. Compounds 15 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 >£45 million 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 ‘live’
- 16. PharmaSci 16 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
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- 18. Siloed data lookslike… 18
- 19. 19 Let’s build bridgesto connect “siloes” of interest… 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
- 20. 20 Let’s build bridgesto connect “siloes” of interest… The diagram shows the resulting subgraph for the federated query that answers the question “Find all genes in BIKG linked with a specific disease, and then all trials in CIKG that are testing drugs targeting those genes” Biological Insights Knowledge Graph Competitive Intelligence Knowledge Graph CIKG
- 21. Harvesting LLM power to talkto the graphs 21
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- 23. Acknowledgments • Aaron Holt •Nicolas Mervaillie • Joe Depeau • Job Maelane • Yuen Leung Tang • Jesus Barrasa • Daniel Addison • Delyan Ivanov • Suzy Jones • Wolfgang Klute • Michael Lainchbury • Andriy Nikolov • Nishank Mahore • Cristina Mihetiu • Justin Morley • Michaël Ughetto • Lauren Eardley • Karen Roberts • Anthony Puleo • Cinthia Willaman • Ivan Figueroa • Carlos Mercado • Jorge Gutierrez • Koushik Srinivasan
- 24. Enterprise Data Office| IGNITE Enterprise Knowledge Graph Service Robert Hernandez Knowledge Engineering Lead Sandra Carrasco Senior Knowledge Graph Engineer Antonio Fabregat Knowledge Graph Lead Ronnie Mubayiwa Senior DevOps Engineer Varun Bhandary Senior Solution Architect Sree Balasubramanyam Senior IT Project Manager Vishal Kumar DevOps Engineer Preetha Mutharasu Knowledge Graph Engineer Prem Oliver Vincent Scrum Master Andy Stafford-Hughes Testing Manager Umapathy Boopathy Cloud Solution Architect Pascual Lorente Senior Knowledge Graph Engineer