Knowledge Graphs - Foundations and Applications

Course
  • Big Data and AI

Knowledge Graphs - Foundations and Applications

Despite the fact that it affects our lives on a daily basis, most of us are unfamiliar with the concept of a knowledge graph. When we ask Alexa about tomorrow's weather or use Google to look up the latest news on climate change, knowledge graphs serve as the foundation of today's cutting-edge information systems. In addition, knowledge graphs have the potential to elucidate, assess, and substantiate information produced by Deep Learning models, such as Chat-GPT and other large language models. Knowledge graphs have a wide range of applications, including improving search results, answering questions, providing recommendations, and developing explainable AI systems. In essence, the purpose of this course is to provide a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world.

Offered by Prof. Dr. Harald Sack | Last updated on Sep 17, 2026 | 8989 enrollments

  • Expert
  • EN
  • Certificate

Course information

A knowledge graph is a structured representation of knowledge that is used to provide a comprehensive and interconnected view of a specific domain. In this course we will discuss the key features and characteristics of knowledge graphs. You will learn all what is necessary to design, implement, and apply knowledge graphs. The focus of this course will be on basic semantic technologies including the underlying principles of knowledge representation and symbolic AI. This includes information encoding via RDF triples, knowledge representation via ontologies with OWL, efficiently querying knowledge graphs via SPARQL, latent representation of knowledge in vector spaces, as well as knowledge graph applications in innovative information systems, as e.g., semantic and exploratory search. Furthermore the role of knowledge graphs in artificial intelligence and machine learning will be discussed, as well as their potential to improve explainability and trustworthiness of "black box" deep learning models such as Chat-GPT.

Attention: This course is currently in self-study mode, in which you do not have access to graded assignments/exams. Therefore, we can only issue you a certificate of participation.

What you'll learn

  • Basic understanding of knowledge graphs
  • Basic understanding of ontologies
  • Basic understanding of Semantic Web Technologies
  • Basic understanding of ontology design and knowledge graph construction
  • Basic understanding of knowledge graph embeddings

Course contents

  • Week 1

  • Week 2

  • Week 3

  • Week 4

  • Week 5

  • Week 6

  • 7 Final Exam

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Certificate Requirements

  • Gain a Certificate by earning at least 50% of the maximum number of points from all graded assignments.
  • Gain a Confirmation of Participation by completing at least 50% of the course material.

Find out more on our page for certificates and guidelines.

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