Knowledge graphs are driving industry disruption and business transformation by bringing together previously disparate data, using connections for superior decision support, and adding context for more intelligent applications (including AI). In this session, we walked through the fundamental elements of knowledge graphs including contextual relevance, dynamic self-updating, understandability with intelligent metadata, and the combination of heterogeneous data.
Nav Mathur discussed the 3 main types of knowledge graphs (context-rich search, external insights sensing, and enterprise NLP) that build on each other, and how and why real-world organizations are utilizing graphs as the building blocks for their applications. Also covered were ideas on how to start building analytical applications on top of your knowledge graph using Neo4j Solution Frameworks quickly and easily.
See real world knowledge graphs and walk away with practical approaches for building your knowledge graph to leverage it for business applications.