
This is the very first lecture of this course, where we'll go through introductions and set the scene for the course.
This is a simple activity where you will use a visual graph to navigate through different content topics.
In this lecture, we'll go through some important ideas that are central to knowledge graphs and that we can draw out from the previous activity.
This lesson provides a concise definition for what a Knowledge Graph is.
This lesson clarifies the course structure, its intended audience and highlights all the key learning outcomes you will benefit from.
Here, you will find a decision tree diagram that will help you decide whether this course is really what you are after.
This lecture explores the idea of a network of data, which is a fundamental building block of a knowledge graph.
This lecture explores the idea of weaving data using explicitly identifiable relationships in order to bring meaning to connections.
With the interwoven 'mesh' of information, it's possible to add business rules to a knowledge graph to support knowledge discovery. This is the idea of inferable knowledge - a key strength of knowledge graph.
Knowledge graph schemas are flexible and easy to incorporate and amend. In this lecture, we introduce the concept of 'ontology', a specialist term to mean a knowledge graph schema.
In this activity, you will watch a short TED Talk about "A Visual History of Human Knowledge" by Manuel Lima.
This lecture explores the importance of connections and why they matter for transforming data into knowledge.
Knowledge graph owes its origin to Graph Theory. In this lecture, we briefly touch on this aspect.
Here, we will explore the different core components of the knowledge graph architecture.
In this lecture, you will be able to recognise the key differences between knowledge graph-based systems and relational database management systems.
In this lesson, we will look at the graph technology landscape. This landscape is getting busier and busier with several vendors and companies delivering various capabilities and use cases.
Agile development methodology can be applied to knowledge graph development. This lecture focuses on the Scrum methodology for developing knowledge graphs, backed by useful examples.
This is a continuation of the previous lecture looking at different approaches for when to deploy ontologies as part of the build methodology.
This lecture discusses the Linked Data use case.
This lecture discusses the Semantic Search use case.
This lecture discusses the Semantic Federation use case as part of the bigger theme of semantic interoperability.
This lecture discusses the Enterprise Knowledge Graph use case.
Generative AI and LLMs can hallucinate. To prevent this from happening, we can make use of knowledge graphs as part of a RAG architecture.
This lecture discusses various other use cases for knowledge graph.
This lecture touches on the place knowledge graph technology occupies in data architecture, its relevance to AI and Machine Learning, the concept of explainability, and how users are intended to consume information coming from knowledge graph through familiar views.
This is the concluding lecture for this series. Hope you've enjoyed the course!
Download course slides.
Attributions, special thanks and disclaimer.
Knowledge graphs are reshaping how organisations store, connect and reason with data. Here's where your journey starts.
If you've tried to learn about knowledge graphs before, you'll know the problem: the available materials tend to be scattered, highly technical and written for people who are already halfway there. This course was built to fix that — a carefully curated, accessible introduction that gives complete beginners a genuine foothold in one of the most exciting areas of modern data technology.
So what exactly is a knowledge graph? At its core, it's a network of facts connected through explicitly defined relationships — one that doesn't just store data, but allows new knowledge to be inferred from it. Underpinning knowledge graphs is a technology stack that unlocks powerful capabilities: tearing down data silos, richly representing data and metadata, embedding meaning into computation through semantics, and driving next-generation AI and analytics.
Organisations across manufacturing, telecommunications, IT, mass media, financial services, pharmaceuticals and beyond are already applying knowledge graph technology to power their data strategies and digital transformation. Increasingly, knowledge graphs are also playing a critical role in making AI work reliably over enterprise data — providing the structured, semantically rich foundation that grounds large language models, reduces hallucinations and enables AI systems to reason over real organisational knowledge rather than generic training data. Knowledge graphs sit at the heart of Industry 4.0, Digital Twins, explainable AI and intelligent decision support — and understanding them is fast becoming an essential skill for any data professional.
What you will be able to do after this course:
Explain what knowledge graphs are and articulate why they matter to modern organisations
Speak confidently using the vocabulary of knowledge graphs, ontologies and semantics
Describe the technology stack that underpins knowledge graphs and how its layers connect
Identify real-world industry applications and recognise opportunities for applying graph thinking
Use this course as a confident springboard into more advanced study in semantic technologies
Who this course is for:
This course is designed for complete beginners — data professionals, business analysts, architects and anyone with a curiosity about the latest trends in information modelling, data architecture and knowledge representation. No prior exposure to knowledge graph technologies is required. If you've heard the term and wondered what it actually means, or if you want to understand why so many organisations are investing in this space, this is exactly the right place to start.