Knowledge Graphs & Graph Technologies (2026) | Graph Databases, Ontologies, GNNs & GraphRAG

Foundations

4 entries

Knowledge graphs, knowledge engineering and semantic layers.

01Knowledge Graphs (KGs)Enterprise knowledge graph platforms, graph databases, applications, GraphRAG tools and W3C standards. 02Knowledge EngineeringData fabric software, knowledge graphs, graph data platforms, semantic graphs, RDF databases and property graphs. 03Knowledge Engineering Start-upsSQL knowledge graph platforms and semantic database pioneers such as Timbr and TypeDB (formerly Grakn). 04Semantic Layers & Metric StoresSemantic layer and metric store platforms such as AtScale, Cube, Snowflake, Looker, Fabric, dbt, Databricks and SAP.

Graph Databases & Platforms

3 entries

Where graph data is stored, queried and analysed.

05Graph Database Management SystemsGraph databases including Neo4j, ArangoDB, Amazon Neptune, TigerGraph, JanusGraph, NebulaGraph and Oracle Graph. 06Graph Data PlatformsProperty graph databases, graph analytics engines, managed cloud graph services and query frameworks for connected data. 07Graph Intelligence PlatformsTools for graph databases, knowledge graphs, GraphRAG, decision intelligence and threat-intelligence graphs.

Semantic Graphs & RDF

2 entries

Triple stores, ontologies and the semantic web stack.

08OntologiesOntology resources: OWL, RDFS, SKOS and SHACL standards, editors like Protégé, reasoners, libraries, registries and reference ontologies. 09Semantic Graphs & RDF DatabasesSemantic graph and RDF platforms such as AllegroGraph, Apache Jena, GraphDB, MarkLogic, Stardog, Virtuoso and Amazon Neptune.

Graph Query Languages

1 entries

Asking questions of connected data.

10Graph Query Languages (GQLs)Query languages including Gremlin, GQL, openCypher, Cypher, GSQL, PGQL, AQL and SPARQL for property graphs and RDF data.

Graph Machine Learning

4 entries

Learning from nodes, edges and structure.

11Graph Neural NetworksHow GNNs learn from connected data through message passing, and the GCN, GAT, GraphSAGE and GIN architectures. 12Understanding Graph Neural NetworksA practitioner's blog on message passing, core GNN architectures, applications and where the field is heading. 13GNN FrameworksFrameworks and graph analysis libraries such as PyTorch Geometric, DGL, Spektral, Jraph and NetworkX. 14GNN Open-Source LibrariesOpen-source libraries for deep learning on graphs, including PyTorch Geometric, DGL, Spektral and Jraph.

GraphRAG & Related Resources

3 entries

Retrieval, databases and reports.

15Trends in the RAG SpaceHow retrieval-augmented generation evolved from naive pipelines to agentic, multimodal, real-time retrieval architectures. 16Database SystemsThe full directory of database categories, including graph, vector and multi-model databases. 17Semantic Layers & the Economics of AI on the Warehouse (PDF)Report on semantic layers and the economics of running AI on the data warehouse.

Frequently Asked Questions

What is a knowledge graph?

A knowledge graph models entities and the relationships between them, with meaning attached through ontologies, so people and AI systems can query connected data in context.

Where should I start?

Begin with the Knowledge Graphs page for platforms and standards, then Graph Database Management Systems for storage, and Ontologies and Semantic Graphs for the RDF stack.

Property graph or RDF?

Property graphs, queried with Cypher or Gremlin, suit application and analytics workloads, while RDF, queried with SPARQL, suits data integration and linked data. The Graph Query Languages page compares them.

How do graphs connect to AI?

Graph neural networks learn directly from connected data, and GraphRAG uses knowledge graphs to give large language models structured context. See the Graph Neural Networks and RAG pages.

Why is one report missing?

A PDF report on GraphRAG and trustworthy AI from Graphwise is not listed, because its file name contains spaces and currently returns an error on the server. It can be added back once the file is renamed.