Get started with GraphDB Fundamentals

GraphDB Fundamentals is a training course designed for those who chose to start with graph databases that apply the W3C standards. It will be delivered in a series of nine videos. Follow their progress on the GraphDB Fundamentals page.

Starting with a Graph Database is Easy

Even though, there are various kinds of graph databases, which use proprietary languages and internal data structures, the W3C has developed a set of standards to boost the integration and exchange of the graph databases applications,

…and eventually to make the life of developers easier.

We strongly encourage everybody who is interested in trends like graph databases, semantic technology and smart data, to spend a couple of hours on this training course and get their hands on GraphDB.


If you have some experience with relational databases, it should not be a big challenge to start with a graph database. Tweet this.

Get to know RDF, SPARQL and Ontology

Following the main W3C standard for graphs – the Resource Description Framework (RDF) – each graph is described as a set of triples: two nodes and an arch between them. This is where the name “triplestore” comes from.

GraphDB is the triplestore graph database used in all examples and hands-on exercises in this training course. Tweet this. 

To get started, you need to familiarize yourself with the three main concepts that differentiate graph databases: RDF triples, SPARQL and Ontology.


RDF triples: the building blocks of your graph.

Each triple is a statement about your data resources, according to the Resource Description Framework (RDF). It consists of a subject (first node), a predicate (arch), and an object (second node), and links to other statements about your data.

SPARQL: an RDF query language.

SPARQL is the main semantic query language, used for retrieving and manipulating data stored in RDF graph databases. Working with it is very efficient as the syntax is intuitive like most SQL-like languages. In addition, the more complex your use cases become, the more excited you will be about the expressiveness and the computational mechanism behind SPARQL.

Ontology: how you model your data

This is the equivalent of a relational schema for your graph. Ontologies organize your data and allow you to formally name and define types, properties and interrelationships in it.

GraphDB Fundamentals training course is useful for everybody who wants to enhance their knowledge of Semantic Technology and be able to work with graph databases.

Milena Yankova

Milena Yankova

Director Global Marketing at Ontotext
A bright lady with a PhD in Computer Science, Milena's path started in the role of a developer, passed through project and quickly led her to product management. For her a constant source of miracles is how technology supports and alters our behaviour, engagement and social connections.
Milena Yankova

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