Graph-enabled search unlocks richer relevance by modeling entities and connections, but many teams struggle to operationalize graphs, optimize traversals, or combine them with semantic techniques.
To win, your search architecture must use graph relationships to deliver deeper, more context-aware retrieval.
Teams exploring graph-enabled search commonly face:
- Shallow relevance: Rely on flat document retrieval that ignores entity relationships and network structure.
- Complex modeling: Struggle to model entities, connections, and traversal patterns that support real search queries.
- Disconnected techniques: Treat graph and semantic search as separate systems instead of complementary approaches.
Without graph-aware design, search systems miss critical context and fail to surface meaningful connections.
In this hands-on workshop, your team designs graph-enabled search approaches that model relationships, optimize traversals, and integrate semantic techniques.
- Explain core graph search fundamentals and how they differ from document-centric retrieval.
- Model entity relationships and connections to support richer search behavior.
- Optimize graph traversals for efficient and relevant search queries.
- Enrich search experiences using ontologies and tagging strategies.
- Combine graph and semantic techniques to improve contextual relevance.
- Explaining Graph Search Fundamentals
- Modeling Entity Relationships and Connections
- Optimizing Traversals for Search Queries
- Enriching Search with Ontologies and Tags
- Combining Graph and Semantic Techniques
- Explaining Graph Search Fundamentals
- Modeling Entity Relationships and Connections
- Optimizing Traversals for Search Queries
- Enriching Search with Ontologies and Tags
- Combining Graph and Semantic Techniques
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate engineers with search or data modeling experience
Graph data models, traversal patterns, and integrated search techniques