Resources for Learning Linked Data

Linked Data Introductions

Introduction to Linked Data from opendata.swiss. Web page with a compact introduction to Linked Data, also suitable for data owners.
TED Talk by Tim Berners-Lee. Passionate plea for Linked Data as an idea from the founder of the World Wide Web (duration: 17 minutes).
Europeana Linked Data Introduction. Short introduction to Linked Data mainly from the perspective of memory Institutions (duration: 4 minutes).
Semantic Web for the Working Ontologist. This book gives a comprehensive introduction to Linked Data (510 pages) and can be considered as a standard work for the subject.
Linked Open Data: The Essentials. Introduction to Linked Data with a focus on decision makers. Written by a company engaded to Linked Data (62 pages).
Tim Berners-Lee: Open, Linked Data for a Global Community. Video with a focus on the bottom up nature of Linked Data and the idea to begin small instead of designing the complete system before starting (duration: 10 minutes).
Linked Data Engineering (Semantic Web) OpenHPI by Prof. Dr. Harald Sack. This multiple hour academic and comprehensive video playlist in English will introduce you to the basic principles and technologies of Linked Data to enable data sharing and reuse on a massive scale.

Wikidata SPARQL Tutorial
Linked Data Patterns. A pattern catalogue for modelling, publishing, and consuming Linked Data.
Data Science Group Uni Paderborn: Semantic Web learning resources.
Zazuko Get Started Pages. Entry point into the Linked Data universe for JavaScript and Python developers as well as an introduction to SPARQL.

This section lists a variety of software tools to work with Linked Data.

RML.io: Generate knowledge graphs - Applications for Linux, Windows, and macOS machines for generating knowledge graphs. They rely on declarative rules that define how the knowledge graphs are generated.
CARML: CARML is a java library that transforms structured sources to RDF as declared in an RML mapping, in accordance with the RML spec.
Ontop: Ontop is a Virtual Knowledge Graph system. It exposes the content of arbitrary relational databases as knowledge graphs. These graphs are virtual, which means that data remains in the data sources instead of being moved to another database.