Source
Earlier versions of this training module have been developed within the context of the smeSpire project, 2014 (http://www.smespire.eu).
Ownership
Authors: Diederik Tirry (SADL), Anders Östman (Novogit), Monica De Martino (CNR-IMATI). The material is provided under Creative Commons Attribution Share-Alike License (http://creativecommons.org/licenses/by-sa/3.0/).
Abstract

Linked Data is a web based approach to publish information in a structured way so that it can be interlinked with other information on the web, and thus become more useful. Rather than using web pages for humans to be read, information is presented in such a way that it can be read automatically by computers. This enables data from different sources to be linked and used together. The collection of Semantic Web technologies (RDF, OWL, SKOS, SPARQL, etc.) provides an environment in which applications can query the data, draw inferences using vocabularies, etc.

This seminar introduces the main principles of Linked Data, the underlying technologies and background standards, and how it may be applied in SDI contexts. It provides an overview of how data can be published as linked data (RDF), explains the role of vocabularies and Uniform Resource Identifiers. The module consists of shorter lectures and hands-on exercises. The reading material can be downloaded from the internet in the form of a web lecture.

Structure
  1. Introduction
  2. Publishing Linked Data
  3. Querying Linked Data
  4. Exercises
Learning outcomes

After the training offer, the participant will be able to Identify and describe the basic principles of linked data; apply the guidelines for publishing linked data; understand URI and licensing strategies; understand the use of existing vocabularies and express data in RDF triples and set links to other data sources using OpenRefine.

Intended Audience

This seminar aims at (spatial) data experts that need a profound knowledge and understanding of Linked Data, and that need the skills to publish data as linked data.

Pre-requisites

Introduction to Linked Data.

Language
English
Format
PDF documents, presentations, Web lecture. The seminar includes demonstrations. The module is a self-learning module.
Expected workload
6 hours