Uploaded May 2026 | Updated September 2026, 2 weeks ago
Using ontological models of your data along with spatial and graph analytics in the Oracle AI Database helps digital governments tackle agricultural challenges. Learn how you can harness property graphs and ontologies to create connected, context-rich data models that evolve in real time¿integrating weather patterns, soil health, crop varieties, and resource flows. See how dynamic spatial querying enables policymakers and analysts to interactively explore spatial relationships, track environmental impacts, and uncover trends as geography, time, and data change.
With the fusion of relational, geospatial, graph, and semantic data, users are empowered to visualize and analyze shifting scenarios with expressive SQL. Experience how these converged capabilities in Oracle Cloud and Autonomous AI Database enable AI-driven decision-making, foster resilience in food systems, and unlock actionable insights through interoperable, scalable geographic intelligence for digital government communities.
The Agriculture Case Study. - Your wish 00:04:39
The Disconnected Meaning Problem 00:05:39
Why Traditional RAG Falls Short? 00:06:22
What is Ontology? 00:07:20
Hybrid Graph Architecture 00:09:16
Solving the Agriculture Question Step-by-Step 00:11:30
Why Spatial Intelligence Matters? 00:15:52
Demo 00:21:05
Key Takeaways 00:27:49
Final Message: From Data to Connected knowledge 00:28:47
Using ontological models of your data along with spatial and graph analytics in the Oracle AI Database helps digital governments tackle agricultural challenges. Learn how you can harness property graphs and ontologies to create connected, context-rich data models that evolve in real time¿integrating weather patterns, soil health, crop varieties, and resource flows. See how dynamic spatial querying enables policymakers and analysts to interactively explore spatial relationships, track environmental impacts, and uncover trends as geography, time, and data change.
With the fusion of relational, geospatial, graph, and semantic data, users are empowered to visualize and analyze shifting scenarios with expressive SQL. Experience how these converged capabilities in Oracle Cloud and Autonomous AI Database enable AI-driven decision-making, foster resilience in food systems, and unlock actionable insights through interoperable, scalable geographic intelligence for digital government communities.
The Agriculture Case Study. - Your wish 00:04:39
The Disconnected Meaning Problem 00:05:39
Why Traditional RAG Falls Short? 00:06:22
What is Ontology? 00:07:20
Hybrid Graph Architecture 00:09:16
Solving the Agriculture Question Step-by-Step 00:11:30
Why Spatial Intelligence Matters? 00:15:52
Demo 00:21:05
Key Takeaways 00:27:49
Final Message: From Data to Connected knowledge 00:28:47
