• Difference between relational data and graph-based data
• Business use cases for knowledge graphs
• Role of knowledge graphs in AI and analytics
• Overview of SAP HANA Cloud Knowledge Graph Engine
• Integration with SAP HANA Cloud database
• RDF, triples, and graph storage concepts
• Query and reasoning architecture
• Architecture supports semantic data processing
• Ontology concepts: classes, properties, relationships
• Namespace and vocabulary management
• Designing enterprise knowledge models
• Modeling ensures meaningful semantic relationships
• Mapping relational data to graph structures
• Handling structured and semi-structured data
• Data versioning and lifecycle management
• Data management ensures consistency
• Writing basic and advanced graph queries
• Filtering, joins, and pattern matching
• Query performance considerations
• Querying enables semantic insights
• Ontology-based reasoning
• Deriving implicit knowledge from data
• Use of reasoning in business scenarios
• Semantic processing enhances intelligence
• Integration with SAP Analytics and AI services
• Enriching AI models with semantic context
• Knowledge graphs for GenAI grounding
• Integration enables intelligent insights






















