GraphDB Fundamentals Video Series

This video series builds the basis for working with graph databases that implement the W3C standards and particularly GraphDB. It is a training class delivered in a series of ten videos that will accompany you in your first steps of using triplestore graph databases.

Module 1: RDF & RDFS

RDF is a standardized format for graph data representation. This module introduces RDF, what RDFS adds to it, and how to use it by easy-to-follow examples from “The Flintstones” cartoon.

Module 2: SPARQL

SPARQL is a SQL-like query language for RDF data. It is recognized as one of the key tools of the semantic technology and was made a standard by W3C. This module covers the basis of SPARQL, sufficient to create you first RDF graph and run your first SPARQL queries.

Module 3: Ontology

This module looks at ontologies: what is an ontology, what kind of resources does it describe, and what are the benefits of using ontologies. Ontologies are the core of how we model knowledge semantically. They are part of all Linked Data sets.

Module 4: GraphDB Installation

This video guides you through the steps of setting up your GraphDB: from downloading and deploying it as a native desktop application, a standalone server, or a Docker image, through launching the Workbench, to creating a repository and executing SPARQL queries against the data in it. Our favorite example from The Flintstones is available here as data for you to start with.

Module 5: GraphDB Workbench & REST API

GraphDB Workbench is a web-based administration tool that allows you to manage GraphDB repositories, load and export data, monitor query execution, develop and execute queries, manage connectors and users. The GraphDB REST API can be used to automate various tasks without having to open the Workbench in a browser and doing them manually. This makes it easy to script cURL calls in your applications. In this video, we provide a brief overview of their main functionalities that you will be using most of the time.

Module 6: Loading Data

Data is the most valuable asset and GraphDB is designed to store and enhance it. This module shows you several ways of loading individual files and bulk data, as well as how to RDF-ize your tabular data and map it against an existing ontology.

Module 7: Rulesets & Reasoning Strategies

This module outlines the reasoning strategies (how to get new information from your data) as well as the rulesets that are used by GraphDB. The three different reasoning strategies that are discussed are: forward chaining, backward chaining, hybrid chaining. They support various GraphDB reasoning optimizations, e.g., using owl:sameAs.

Module 8: Virtualization

This module walks you through GraphDB’s data virtualization functionality, which enables direct access to relational databases with SPARQL queries, eliminating the need to replicate data. To achieve this, GraphDB integrates the open-source Ontop project and extends it with multiple GraphDB-specific features.

Module 9: Plugins

This video covers the GraphDB plugins — externally provided libraries allowing developers to extend the engine. They can synchronize their internal state over the public GraphDB Plugin API and handle the execution of registered triple patterns. Plugin examples include RDF Rank, Geospatial extensions, and more.

Module 10: Connectors

The Lucene, Solr, Elasticsearch, and OpenSearch GraphDB connectors enable the connection to an external component or service, providing full-text search and aggregation. The MongoDB integration allows querying a database using SPARQL and executing heterogeneous joins, and the Kafka GraphDB connector provides a means to synchronize changes to the RDF model to any downstream system via the Kafka framework. This module explains how to create, list, and drop connector instances in GraphDB.