Module 0. About the Course (7 min)

Module 1. Definitions and Business Case (55 min)

• History of Knowledge Graphs
• Knowledge Graphs in Various Industries - Finance
• Knowledge Graphs in Various Industries - Media
• Knowledge Graphs in Various Industries - Agriculture
• Knowledge Graphs in Various Industries - Pharmaceuticals
• Data Management Challenges in the Enterprise

Module 2. Knowledge Graph Supporting Technologies (40 min)

• Features of Graph Data Systems
• Approaches to Graphs as Data
• Property Graph Data Capabilities
• Graph Data Queries
• Graph Data Visualization
• Graph Data Systems
• Standardizing Data

Module 3. Semantic Technology Fundamentals (58 min)

• Knowledge Graph Stack
• RDF Brings Data Together
• RDFS Enhance Data with Types & Properties
• OWL Provides Precision Logic to Describe Models
• SPARQL Lets you Ask Questions About the Data
• Example Knowledge Graph Capabilities Using Semantic Web Standards
• Using SKOS for Knowledge Management
• The Meaning of Meaning

Module 4. Knowledge Graph Enterprise Framework (35 min)

• Application Vs. Enterprise Data
• The Enterprise Data Jungle
• Sustainable Extensibility
• Enterprise Data Community
• Prerequisites for Distributed Data
• Common Reference
• Connecting References
• Semantic Alignment
• Things vs. Strings
• Tools and Components
• Applications of Knowledge Graphs

Module 5. Modeling Methodology and Architecture (33 min)

• Modeling Methodology and Architecture
• Terminology Sources
• Guidelines for Terminology

Module 6. Implementation Fundamentals of Knowledge Graph (47 min)

• Example Implementation
• Incremental Development
• Knowledge Graph Implementation
• Presentation Approach
• Select a Use case
• Inventory Data Sources
• Draw on Reference Ontologies
• Identify Controlled Vocabulary
• Map Metadata to Ontologies
• Materialize Data as Needed
• Build Queries to Respond to Business Questions
• Repeat from Step 3 with New Data
• How Can This Fail?
• Summary: Incremental Development