The Two Worlds of Structured Data
The World of Tables (SQL)
Rigidly structured in rows and columns. Powerful, ubiquitous, and the backbone of transactional systems for decades. Data is grouped by table name.
The World of Graphs (RDF)
Flexibly structured as nodes and relationships. Ideal for representing complex, interconnected knowledge where context is king. Data is a collection of entity relationships.
The Superpower: Unlocking a Web of Data with Linked Data Principles
This isn't just format conversion. Hyperlinks as entity names are globally unique while also providing data (content) lookups and linkability across public or private data spaces (databases, knowledge graphs, filesystems).
Name
Entities with Links
Name an entity of interest (e.g., a specific customer) using a hyperlink.
Name
Attributes with Links
Name attributes (e.g., 'hasAddress') using hyperlinks to share a relationship's meaning or reuse definitions from existing vocabularies and ontologies.
Name
Values with Links
Name attribute-values using hyperlinks to create a public or private Web of connected knowledge.
Virtuoso: Magic In The Middle That Makes It Happen.
Courtesy of its native support of Linked Data Principles, leveraging Hyperlinks as Super-Keys for generating HyperData, costly and complex data migration are now a thing of the past. Virtuoso enables the creation of conceptual, unified views over existing systems. It doesn't move the data; it creates a live, queryable virtual Knowledge Graph over your existing data sources, built on a foundation of open standards.
How It Works: 4-Step Process
1. Connect
Attach any ODBC or JDBC-accessible database table to Virtuoso.
2. Request
Use natural language to request virtual Knowledge Graph generation from designated tables or views.
3. Generate
Virtuoso uses your instructions to create a Live Virtual Knowledge Graph.
4. Interact
Use Natural Language, SQL, SPARQL, or GraphQL to interact with your newly generated Knowledge Graph.
Why This Matters: Your Strategic Advantage
- Unified Access: Query disparate data sources using SQL, SPARQL, or GraphQL; even better, using natural language via MCP-compliant LLM-based AI Agents.
- No Rip & Replace: Maximize the value of your existing technology investments without costly migrations.
- Enhanced Data Quality: Add rich, semantic context to raw data, making it more meaningful and discoverable.
- Future-Proof Architecture: Build on a foundation of stable, open standards.
- Serendipitous Discovery: Uncover valuable relationships and insights you never knew existed.