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AstraZeneca at Neo4j GraphSummit London 14Nov23.pptx
- 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 Data Management • 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
- 4. Tackling Data Growth with 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 vs Traditional 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 Machine Learning 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 and Interconnectivity 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 and Discovery 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 representation alternatives 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 a Service 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 of the AZ´s Knowledge Graph Project Landscape 13
- 14. Biology | Market Strategy | 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) Combine several 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 formulation design 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
- 18. Siloed data looks like… 18
- 19. 19 Let’s build bridges to 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 bridges to 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 talk to the graphs 21
- 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
Editor's Notes
- The rapid growth of data generated from various sources, including IoT devices, social media, and business applications, has led to an increasing need for efficient data management and analysis tools. Knowledge graphs help to organise and structure this vast amount of data, making it easier to access and analyse.
- Traditional data management systems often struggle to capture the complex relationships and semantics within data. Knowledge graphs excel at representing and understanding the meaning and context of data, allowing for more effective analysis and insights.
- The increasing use of AI and machine learning in data analysis requires more efficient ways to represent and process data. Knowledge graphs provide an enriched data model that allows AI and machine learning algorithms to better understand and analyse data.
- Knowledge graphs enable seamless integration of data from various sources, creating a unified view of information. This interconnectivity helps overcome data silos, allowing organizations to derive more value from their data assets.
- Knowledge graphs improve search and discovery capabilities by enabling more intuitive query languages and uncovering hidden relationships within data. This results in more accurate and relevant search results, enhancing user experience and productivity.
- Knowledge graphs support personalized and targeted recommendations by understanding user preferences, behaviour, and the context of their interactions. This leads to more relevant recommendations and increased user engagement.
- Growing importance of knowledge graphs in data management and analysis. There is need for an efficient and reliable service to support both hosting and development of knowledge graphs. AZ is investing in this area to create a robust and scalable service.
- When we talk about multiple siloed databases, we could imagine an archipelago. At the first glance, visiting all islands, doesn't seem an easy task!
- With the right infrastructure, multiple islands can be connected, and visiting them, suddenly, becomes way easier. Federating queries, across siloed databases, is like building bridges between islands. This allows running queries against multiple siloed data sources, without needing to migrate all data to a unified system.