Knowledge Graphs: Data in Context for Responsive Businesses

Applying knowledge in the right context is the most powerful lever businesses can use to become agile, creative, and resilient. Knowledge graphs add context, meaning, and utility to business data. They drive intelligence into data for unparalleled automation and visibility into processes, products, and customers. Businesses use knowledge graphs to anticipate downstream effects, make decisions based on all relevant information, and quickly respond to dynamic markets. In this report for chief information and data officers, Jesus Barassa, Amy E. Hodler, and Jim Webber from Neo4j show how to use knowledge graphs to gain insights, reveal a flexible and intuitive representation of complex data relationships, and make better predictions based on holistic information. Explore knowledge graph mechanics and common organizing principles Build and exploit a connected representation of your enterprise data environment Use decisioning knowledge graphs to explore the advantages of adding relationships to data analytics and data science Conduct virtual testing using software versions of real-world processes Deploy knowledge graphs for more trusted data, higher accuracies, and better reasoning for contextual AI Foreword 1. Introduction What Are Graphs? The Motivation for Knowledge Graphs Knowledge Graphs: A Definition 2. Building Knowledge Graphs Organizing Principles of a Knowledge Graph Plain Old Graphs Richer Graph Models Knowledge Graph Using Taxonomies for Hierarchy Knowledge Graph Using Ontologies for Multilevel Relationships Which Is the Best Organizing Principle for Your Knowledge Graph? Organizing Principles: Standards Versus Custom Essential Capabilities of a Knowledge Graph 3. Data Management for Actionable Knowledge Relationships and Metadata Make Knowledge Actionable The Actioning Knowledge Graph The Data Fabric Architecture Metadata Management Popular Use Cases for Actioning Knowledge Graphs Increased Trust and Radical Visibility 4. Data Processing for Driving Decisions Data Discovery and Exploration The Predictive Power of Relationships The Decisioning Knowledge Graph Graph Queries Graph Algorithms Graph Embeddings ML Workflows for Graphs Graph Visualization Decisioning Knowledge Graph Use Cases Boston Scientific’s Decisioning Graph Better Predictions and More Breakthroughs 5. Contextual AI Why AI Needs Context Data Provenance and Tracking for AI Systems Diversifying ML Data Better ML Processes Improving AI Reasoning The Big Picture 6. Business Digital Twin Digital Twins for Secure Systems Digital Twin for the Win! 7. The Way Forward