Course Description

Knowledge graphs are a universal means of data representation which can be consumed by humans and machines alike, and is therefore a key ingredient in many modern data-driven systems and AI applications, which often need knowledge about the domain they operate in, and/or general purpose knowledge about the world. Knowledge graphs are increasingly used in companies and large organizations, with the most well-known application being the Google Knowledge Graph backing the search engine we all use on a day to day basis. There are also quite a few large-scale open knowledge graphs, like DBpedia or Wikidata, which can be freely used to fuel powerful AI applications.

This course gives an introduction to the underlying standards of knowledge graphs, including knowledge representation and query languages, as well as logical inference. More specifically, it covers the following contents:

  • History of Knowledge Graphs and the Semantic Web
  • Graph Representation Languages (XML, RDF)
  • Graph Inference and Logical Reasoning (RDF Schema, OWL)
  • Knowledge Modeling: Ontologies, Linked Data, and Property Graphs
  • Knowledge Integration
  • Data Quality in Knowledge Graphs
  • Commercial and Open Source Tools and Systems

Besides theoretical lectures, the course also consists of hands-on exercises and a practical project in which you build a small knowledge graph backed application.

Exam Review

The exam review for HWS 2023 will take place on Thursday, February 29th, at 10 am, in room B6 27-29 C101. In case you want to review your exam, please register for the exam review with Bianca Lermer.


  • Java or Python programming skills are required to pass this course!
  • Preferably, some experience with software development
  • To pass the course you have to fulfill the following requirements:
    • Pass the final exam (you have to get a 4.0 or better in the exam to pass this course)
    • Successfully work in a group on a project idea (programming!), present the results and write a report
  • The final grade is the grade achieved in the final exam, however, the project is a mandatory requirement to pass the course.



  • Lecture: Tuesday, 15:30 – 17:00, B6 26, room A1.01
  • Exercise: Friday, 12.00 – 13.30, A5 6, room A5 C015 <- changed!


04.09.2023Lecture: IntroductionExercise: Introduction
11.09.2023Lecture: Representing Knowledge in Graphs (RDF)Exercise: Representing Knowledge in Graphs (RDF)
18.09.2023Lecture: Lighweight Knowledge Graph Inference (RDFS)Exercise: Lighweight Knowledge Graph Inference (RDFS)
25.09.2023Lecture: Linked Data, Knowledge Graph ProgrammingExercise: Linked Data, Knowledge Graph Programming,
Kick Off Group Projects
02.10.2023HolidayNo exercise, time to work on group projects
09.10.2023Lecture: Querying Knowledge GraphsExercise: Querying Knowledge Graphs
16.10.2023Lecture: Public Knowledge GraphsExercise: Public Knowledge Graphs
23.10.2023Lecture: Labeled Property GraphsExercise: Labeled Property Graphs


Lecture: Advanced Knowledge Graph Inference (OWL Part 1)Exercise: Advanced Knowledge Graph Inference (OWL Part 1)
06.11.2023No lecture (time to work on projects)No exercise (time to work on projects)
13.11.2023Lecture: Advanced Knowledge Graph Inference (OWL Part 2)Exercise: Advanced Knowledge Graph Inference (OWL Part 2)
20.11.2023Lecture: Knowledge ModelingExercise: Knowledge Modeling
27.11.2023Lecture: Knowledge Graph Quality and Knowledge IntegrationExercise: Knowledge Graph Quality and Knowledge Integration
04.12.2023Project Presentations 

Important dates for the group projects:

  • Sunday, October, 8th, 23:59: Submission of project proposals
  • Sunday, December 10th, 23:59: Submission of final reports