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Knowledge Hub

Knowledge Engineering: Knowledge Graphs, Ontologies & Graph Platforms

A hub of knowledge engineering directories — the knowledge graphs, ontologies and graph databases used to represent knowledge, and the data engineering around them.

20 topics•5 categories•Updated October 2026

Knowledge Graphs & Semantics

4 entries

Representing meaning and relationships.

01Knowledge Graphs (KGs)Knowledge graph platforms, GraphRAG and standards. 02Knowledge Graphs & Graph TechnologiesGraph databases, ontologies and GNNs. 03OntologiesOntology languages, editors, reasoners and repositories. 04Semantic Graphs & RDF DatabasesRDF stores and semantic graph databases.

Graph Databases & Platforms

4 entries

Storing, analysing and serving graph data.

05Graph DatabasesGraph database systems. 06Graph Data PlatformsGraph analytics, visualisation and managed services. 07Graph Intelligence PlatformsGraph intelligence tools. 08Vector DatabasesVector databases for AI search.

Data Fabric & Integration

3 entries

Connecting data around knowledge.

09Enterprise Data Fabric SoftwareData fabric platforms. 10Data Integration ToolsETL, ELT and integration tools. 11Data EngineeringData engineering hub.

Companies

1 entries

Start-ups in the field.

12Knowledge Engineering Start-upsStart-ups applying knowledge engineering.

Related Pages

8 entries

Neighbouring directories.

13Machine LearningMachine learning hub. 14Deep LearningDeep learning hub. 15Data AnalyticsData analytics hub. 16Data ScienceData science hub. 17Artificial IntelligenceAI hub. 18Generative AIGenerative AI hub. 19Agentic AIAgentic AI hub. 20Computing ParadigmsComputing paradigms hub.

Frequently Asked Questions

What is knowledge engineering?

Knowledge engineering is the discipline of capturing, structuring and representing knowledge, with ontologies, knowledge graphs and rules, so software can reason with it.

What is a knowledge graph?

A graph of entities and the relationships between them, with a schema or ontology that gives the data meaning; used for search, recommendation, analytics and grounding AI.

How do ontologies differ from knowledge graphs?

An ontology defines the concepts and rules of a domain; a knowledge graph holds the actual data organised by such a model.

How does this relate to generative AI?

Knowledge graphs ground language models with structured facts, an approach called GraphRAG, and help agents keep consistent memory.

Where should I start?

Read the Knowledge Graphs page for an overview, then Ontologies for modelling and Graph Databases for storage.