Graph is a native feature of Oracle AI Database. Graphs help you
- Discover connections and explore relationships in data using graph queries to identify patterns
- Analyze your data with graph algorithms such as ranking, centrality, community detection and path finding, to uncover new insights based on how your data is connected
- Create a semantic layer represented as a knowledge graph to help capture better context about your data for AI and AI agents
- And much more.
Best of all, you can create graphs from your existing business data in relational tables, without moving data to a specialized database. As a first class object in Oracle AI Database graphs benefit from scalability, security, high availability, disaster recovery, and other enterprise features.
Developers, data scientists and analysts can use graphs to address various use cases, including financial fraud detection, manufacturing traceability, recommendation systems, semantic data catalogs, and more.
Oracle Graph includes support for
- Property Graph: Create and query graphs using SQL (SQL:2023 standard includes syntax for property graphs)
- RDF Graph: Create, query and run entailment on RDF graphs using the W3C standards (RDF, RDFS, OWL, SPARQL)
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