4-Week Video Course

Linked Data: A Web of Knowledge

From isolated databases to an interconnected Web of Data. Master RDF, SPARQL, ontologies, and real-world semantic web applications — no prior knowledge required.

4 Weeks · 16 Lessons ~3 hrs total Beginner–Intermediate 2 Hands-On Projects
Progress
6% Complete
Person Bob Album Event Place
WEEK 1 · LESSON 1
What Is Linked Data?
Click to watch on YouTube
Week 1 Foundations

What Is Linked Data?

Linked Data is a method of publishing, sharing, and connecting data on the Web so that information from different sources can be understood and used together by both humans and computers. This lesson introduces the core concept and why it represents a paradigm shift from the current document-centric web.

The current web is a giant collection of documents linked by hyperlinks — humans read them, but machines struggle to understand the meaning inside. Linked Data transforms the web into a global database where pieces of information are connected through explicit, machine-readable relationships.

Instead of just "reading" a page about a musician, a computer can understand: this person is a musician → they were born in City X → they wrote Album Y → Album Y was released in 1997. Each fact is a node; each relationship is an edge — forming a web of knowledge.

Subject
Sally
Predicate
employed by
Object
Target

↑ The RDF triple — the fundamental unit of Linked Data

🔗
Data Integration
Break down data silos by connecting isolated datasets from CRM, HR, and inventory systems without migrating to a single database.
🧠
Semantic Web
The foundational technology for the Semantic Web — where computers understand not just information, but the relationships between it.
🔍
Enhanced Search
Search a scientist and get publications, affiliations, research areas, and collaborators — context-aware results powered by entity graphs.
🕸️
Knowledge Graphs
Google's Knowledge Graph, Wikidata, DBpedia — all built on Linked Data principles, connecting people, places, events and concepts.
🤖
AI & Analytics
AI systems benefit from structured, interconnected data — enabling better reasoning, inference, and recommendation systems.
♻️
Data Reuse
Linking datasets encourages reuse, improves consistency, and contributes to higher overall data quality across organisations.

"Linked Data is a way of publishing and connecting structured information on the web so that computers and humans can easily discover, combine, and reuse it."

Across the course you'll work with Turtle syntax (Week 2), SPARQL queries (Week 3), and real-world JSON-LD (Week 4). Here's a preview of each.

Turtle RDF — Describing a Person

TURTLE (.ttl) 
# Prefixes define shorthand for IRIs
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix ex:    <http://example.org/> .
@prefix schema: <https://schema.org/> .

ex:bob
    a                foaf:Person ;
    foaf:name        "Bob Smith" ;
    foaf:birthDate   "1990-07-04" ;
    foaf:knows       ex:alice ;
    schema:worksFor  ex:target-corp .

SPARQL — Query for Employees

SPARQL 
PREFIX ex:     <http://example.org/>
PREFIX foaf:   <http://xmlns.com/foaf/0.1/>
PREFIX schema: <https://schema.org/>

SELECT ?person ?name ?org
WHERE {
    ?person  a                foaf:Person ;
             foaf:name        ?name ;
             schema:worksFor  ?org .
    FILTER(?org = ex:target-corp)
}
ORDER BY ?name

JSON-LD — For Your Sports Website (hulako.co.uk)

JSON-LD 
{
  "@context": "https://schema.org",
  "@type": "SportsEvent",
  "name": "Arsenal vs Chelsea",
  "startDate": "2025-12-26T15:00:00",
  "location": {
    "@type": "Place",
    "name": "Emirates Stadium"
  },
  "homeTeam": { "@type": "SportsTeam", "name": "Arsenal FC" },
  "awayTeam": { "@type": "SportsTeam", "name": "Chelsea FC" }
}

Add this to match pages on hulako.co.uk for Google Knowledge Panel integration.

Week 3 Activity
Write and Execute 3 SPARQL Queries on a Local Triplestore
Set up Apache Jena Fuseki locally and run real SPARQL queries against a sports dataset you build yourself. This bridges the theory from Weeks 1–2 into hands-on practice.
1Download Apache Jena Fuseki and start the server with a sample Turtle file.
2Load a dataset of 5–10 football players (or cyclists/tennis players) formatted in Turtle.
3Write a SELECT query to find all players from a specific country.
4Write a FILTER query to return players with more than X goals.
5Use CONSTRUCT to generate a new subgraph linking players to their clubs.
Week 4 Final Project
Publish a Small Linked Data Dataset and Query It Remotely
Apply everything you've learned by publishing a real Linked Data set connected to external knowledge bases like DBpedia and Wikidata — earning full 5-star Open Data status.
1Create a Turtle dataset for your sports domain (players, clubs, results).
2Load into Apache Jena Fuseki or Ontotext GraphDB (cloud).
3Write 3 SPARQL queries demonstrating SELECT, FILTER, and CONSTRUCT.
4Link your entities to DBpedia/Wikidata URIs for 5-Star status.
5Host on GitHub and add JSON-LD markup to your hulako.co.uk match pages.
Tools You'll Use
Apache Jena Fuseki RDFLib (Python) Protégé Wikidata SPARQL DBpedia schema.org JSON-LD Playground SHACL Validator

A complete 4-week structure with 16 lessons (~3 hours of video) plus 2 hands-on projects. Designed for complete beginners — no prior Semantic Web knowledge required.

Module Lesson Duration Format
W1.1 What Is Linked Data? 8 min Concept + Analogy
W1.2 Why Linked Data Matters — 5 Core Benefits 10 min Lecture + Diagrams
W1.3 The Semantic Web Vision 7 min Concept
W1.4 Knowledge Graphs: Google, Wikidata, DBpedia 9 min Case Study
W2.1 Introduction to RDF 11 min Lecture + Demo
W2.2 Triples, IRIs, Literals & Blank Nodes 9 min Coding
W2.3 Serialization: Turtle, JSON-LD, RDF/XML 12 min Hands-On
W2.4 RDFS & OWL: Vocabularies & Ontologies 13 min Lecture + Code
W2.5 Logical Inference & Reasoning with RDFS 10 min Concept + Demo
W3.1 SPARQL Basics & Query Types 10 min Lecture
W3.2 Writing Simple Queries: SELECT, FILTER 12 min Coding
W3.3 Advanced Patterns: JOINs, CONSTRUCT, SERVICE 14 min Coding
W3.P 🔬 Project: SPARQL Queries on Apache Jena Fuseki 20 min Project
W4.1 Linked Data Tools: Browsers, Triplestores, Editors 9 min Overview
W4.2 Real-World Applications: BBC, National Archives, Google 11 min Case Studies
W4.3 5-Star Open Data & The Future Web 8 min Lecture
W4.P 🚀 Final Project: Publish & Link Your Dataset 25 min Final Project
Tim Berners-Lee's 5-Star Open Data Model (Week 4)
★
Available onlineAny format, open licence (e.g. scanned PDF)
★★
Structured dataMachine-readable (e.g. Excel)
★★★
Open formatNon-proprietary (e.g. CSV)
★★★★
Use URIsIdentify things with web URIs
★★★★★
Link your dataLink to other people's data