Why semantic knowledge graphs matter now
We live in an era of vast, fragmented data. Enterprises operate across hundreds of systems, APIs, and formats. AI agents are becoming autonomous decision-makers. But here is the problem: most data is trapped in silos without shared meaning. An "order" in one system is a "transaction" in another. A "customer" in CRM has no relation to the same person in the billing database.
Knowledge graphs built on open semantic standards solve this. RDF (Resource Description Framework), SPARQL, OWL, and SHACL provide a machine-readable, interoperable foundation where data carries its own meaning. Unlike proprietary graph models, RDF-based knowledge graphs are vendor-neutral, standards-compliant, and designed for the open web.
As AI agents increasingly need to reason over structured knowledge - not just retrieve text - the ability to model, query, validate, and infer across linked data becomes a core infrastructure capability, not a niche academic concern.
What the major platforms offer
The large cloud and enterprise platforms have taken varied approaches to graph and knowledge management. Most have invested heavily in property graphs or proprietary ontology models, but very few support the W3C semantic web standards (RDF, SPARQL, OWL, SHACL) that enable true interoperability and reasoning.
Major platform landscape (click to expand)
Oracle RDF Graph
Native RDF triple store inside Oracle Database. Full SPARQL 1.1, OWL 2 EL reasoning, SHACL validation.
Full RDF/SPARQL support
SAP HANA Cloud KGE
Knowledge Graph Engine launched 2025. RDF triples, SPARQL, RDFS/OWL reasoning, SHACL validation inside HANA Cloud.
Full RDF/SPARQL support
Amazon Neptune
Managed graph database supporting both RDF/SPARQL and property graph (Gremlin, openCypher).
Full RDF/SPARQL support
Microsoft Azure / Fabric
Cosmos DB offers property graph only (Gremlin API). Fabric IQ (preview) adds an ontology layer with RDF/XML import/export and OWL class modeling, but queries use GQL/KQL, not SPARQL. No native RDF triple store or SPARQL endpoint.
RDF-adjacent (import/export only)
Google Cloud
Enterprise Knowledge Graph uses schema.org internally but exposes no SPARQL endpoint. Proprietary API only.
No native RDF support
IBM
DB2 had RDF/SPARQL support through v11.1 but removed it in v11.5. No current IBM product supports RDF natively.
Discontinued RDF support
Databricks
No native RDF. OntoBricks (Labs project) adds OWL/R2RML/SPARQL on top, but it is not a core product.
Third-party only
Snowflake
Purely relational/analytical. Can be accessed as a virtual SPARQL endpoint through Ontop, but no native support.
Third-party only
Palantir Foundry
Ontology model inspired by RDF/OWL but implements a proprietary, non-W3C-compliant semantic model. No SPARQL.
Proprietary ontology model
TigerGraph
Property graph only with proprietary GSQL query language. No RDF or SPARQL support.
Property graph only
Neo4j
Dominant property graph database (Cypher). The community
n10s plugin adds RDF import/export and SHACL validation, but triples are converted to labeled property graph structures. No SPARQL, no OWL reasoning.
Elasticsearch
Full-text search engine. Community plugins can index RDF data, but no native semantic support.
No RDF support
MongoDB
Document store. Academic projects translate SPARQL to MongoDB queries, but nothing from MongoDB Inc.
No RDF support
The picture is clear: most major platforms have not adopted W3C semantic standards. Only Oracle, SAP, and AWS offer first-party RDF/SPARQL support. The rest rely on proprietary models that lock knowledge into their ecosystems.
Here is what makes the RDF ecosystem fundamentally different: your data flows seamlessly between any of the tools below. A knowledge graph built in maplib can be queried in GraphDB, validated with pySHACL, visualized in WebVOWL, and served through Fuseki - without a single format conversion or migration script. RDF is a shared data model, not a product. You own your knowledge graph, not the vendor. Switch databases, swap query engines, combine libraries - your triples remain the same. This is interoperability by design, and it means zero vendor lock-in.
Below is the comprehensive landscape of databases, frameworks, and tools that support this open, machine-readable, interoperable approach to knowledge graphs.