Ontop review
An open-source layer for querying relational data as RDF knowledge graphs.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Ontop is an open-source virtual knowledge graph system for teams that need to expose relational database content as RDF without moving the underlying data. It translates SPARQL queries over the virtual graph into SQL executed by relational data sources, making it suited to data integration, semantic querying, and knowledge graph deployments built on existing databases. Ontop supports RDFS and OWL 2 QL ontologies, SPARQL and predefined query endpoints, RDF graph materialization, and both R2RML and native Ontop mappings.
Its strongest fit is an environment with several relational or cloud data sources and a need for a shared semantic layer. The published integration list includes PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, DB2, Snowflake, Databricks, Google BigQuery, AWS Redshift, DuckDB, AWS DynamoDB, TDengine, Denodo, and Dremio. Database federation extends that reach, while virtual lenses let teams define views outside source databases. The Protégé plugin supports mapping design and testing, and Ontop can work across Windows, macOS, Linux, and API deployments.
Ontop is a focused technical choice rather than a hosted commercial service. Its open-source model suits teams that want control over deployment and a semantic interface over existing databases, but the approach depends on relational sources, ontology decisions, and mapping work. Teams choosing Ontop should be prepared to operate the software and shape how source schemas appear in RDF. It is a strong match for semantic querying and virtual knowledge graph projects; teams seeking a broader application suite should consider a different category of product.
Ontop pros and cons
- Where it wins
- Translates SPARQL queries into SQL for existing relational sources
- Supports RDFS, OWL 2 QL, R2RML, and native Ontop mappings
- Connects with databases including PostgreSQL, Snowflake, and BigQuery
- Where it doesn't
- Requires relational databases and mapping work as its foundation
- Its open-source model requires teams to manage deployment themselves
- Its focus is semantic querying rather than a broad application suite
Ontop fact sheet, pricing and score →
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