TRAINING
Hands-on training: How to publish data as Linked data |
Source |
This training module has been developed within the context of the smeSpire project in 2014 (http://www.smespire.eu/). |
Ownership |
Author: Diederik Tirry – KU Leuven. 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. The training session provides an introduction to the linked data and metadata lifecycle and provides insight in how to publish linked data. The training also focuses on the use of vocabularies, URIs and licensing. Hands-on exercises will be given on how the publish data using OpenRefine. |
Structure |
This seminar contains the following modules/parts: 1. Introduction to the linked data and metadata lifecycle 2. Publishing linked data: conceptual and implementation level 3. Use of vocabularies 4. URIs and licensing 5. Hands-on: Publishing data using OpenRefine |
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 - Express data in RDF triples and set links to other data sources using OpenRefine |
Intended Audience |
This seminar aims at technical experts |
Pre-requisites |
Prior knowledge on Linked data is recommended. |
Language |
English |
Format |
Presentation and hands-on training. |
Expected workload |
Expected workload is 8 hours. |