Solution
From SQL to Semantic Graphs:
The Fastest Path to RDF Knowledge
Timbr enables organizations to materialize their SQL ontologies into RDF graphs, unlocking integration with OWL-based systems, triplestores, and property graph platforms like Neo4j and Amazon Neptune.
This dual representation empowers enterprises to standardize logic across data and graph ecosystems, without duplication or complexity.
Behind the Solution
Timbr’s SQL ontology engine allows users to model business concepts, relationships, and hierarchies natively in SQL. With RDF Graph Materialization, these models are exported into RDF/OWL formats for consumption by linked data systems, knowledge graphs, and semantic web tools, giving enterprises a unified foundation for analytics, data integration, and reasoning in SQL, SPARQL, or Cypher.
Challenges
Need to Align SQL and Graph Models?
Many organizations maintain separate modeling layers for relational databases and graph platforms, resulting in duplicate logic, inconsistent relationships, and disconnected pipelines.
Interoperability Gap Between SQL and RDF?
Semantic web standards (RDF, OWL, SPARQL) are powerful but disconnected from SQL-native environments. Without an RDF bridge, sharing or reasoning over enterprise data across systems remains a challenge.
Struggling to Reuse Ontologies in Graph Platforms?
Building ontologies from scratch in RDF or OWL can be tedious and error-prone. Teams using Neo4j, Neptune or triplestores often lack a clear way to import relational semantics in a consistent, governed format.
Why Timbr
SQL-First, RDF-Compatible
Timbr lets you define your semantic model once, in standard SQL, and materialize it into RDF triples. This allows your existing modeling efforts to power both SQL-based analytics and RDF-based reasoning.
Export of Ontology & Metadata
Timbr materializes:
- Concept Hierarchies: Concept hierarchies.
- Relationships and Constraints: Relationships and constraints.
- Attributes and Types: Attributes and types.
- Inferred Logic: Inferred logic and inherited properties.
All exported in standard RDF/OWL formats compatible with tools like Protégé, TopBraid, and any triplestore.
Enable SPARQL, SHACL & OWL Reasoning
With RDF materialization, Timbr models can be imported into reasoning engines, SHACL validators, and linked data pipelines, allowing full compliance with semantic web and linked data practices.
For Neo4j Users: A Semantic Advantage
Timbr acts as a semantic layer for Neo4j, providing ontologies that can be exported as RDF and imported using Neo4j’s neosemantics plugin. This creates a smooth bridge between:
- SQL-Native Modeling: SQL-native modeling in Timbr.
- Property Graph Traversal: Property graph traversal in Cypher.
- RDF Reasoning: RDF reasoning and linked data federation.
With Timbr, Neo4j users can:
- Inherit Semantics: Inherit rich, business-defined semantics without redefining models in Cypher.
- Enable Semantic Enrichment: Enable semantic enrichment of their graphs using external ontologies.
- Standardize Governance: Standardize governance across relational and graph workloads.
Architecture Spotlight
Timbr enables a hybrid environment where:
- Relational Data: Relational data remains in place.
- Timbr Semantic Logic: Timbr provides the semantic logic and RDF export.
- Neo4j RDF Consumption: Neo4j consumes RDF via neosemantics, combining property graph flexibility with governed ontologies.
This architecture empowers scenarios such as:
- Regulatory Compliance: Regulatory compliance using OWL constraints.
- Federated Knowledge Graphs
- LLM Grounding: LLMs grounded in property + semantic graphs.
- Graph Analytics: Graph analytics with inherited business logic.
Impact
Unified Semantics Across SQL and Graph
Timbr ensures your business logic is defined once and reused everywhere, from relational databases to triplestores, graph engines, and GenAI pipelines.
Accelerated Ontology Reuse
Teams can skip manual RDF modeling and directly consume semantically enriched models via standard formats, reducing time-to-graph for analytics, AI, and data integration.
Expand Graph Use Cases Without Rebuilding
Neo4j and other graph users gain immediate access to structured, governed models, fueling smarter relationships, better recommendations, and graph-enhanced AI with semantic fidelity.
