Knowledge Graphs (HWS 2023)

Note: This lecture replaces the old lecture IE 650 Semantic Web Technologies. If you have already attended the lecture Semantic Web Technologies, you cannot attend this one.

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.


  • 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)
21.11.2022Lecture: Advanced Knowledge Graph Inference (OWL Part 2)Exercise: Advanced Knowledge Graph Inference (OWL Part 2)
28.11.2022Lecture: Knowledge ModelingExercise: Knowledge Modeling
05.12.2022Lecture: 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
  • Adminstrative Details

    For attending the course, please register for the lecture in Portal 2 (link to lecture and exercise). The capacity is not limited.

  • Materials and Exercise Sheets


    Lecture Slides:

    05.09.: Organization & Introduction

    12.09.: RDF

    19.09.: RDFS


    08.09.: XML

    15.09.: RDF

    22:09:. RDFS

    29.09.: LOD

    Further material (slides, exercise solutions, videos, and other additional materials) will be made available in the corresponding ILIAS group.

    Literature (suggested reading list):

    • Aidan Hogan et al.: Knowledge Graphs. Morgan & Claypool, 2022, available online
    • Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American, 284 (5), pp. 34–43, 2001
    • Pascal Hitzler, Markus Krötzsch and Sebastian Rudolph. Foundations of Semantic Web Technologies. Chapman & Hall/CRC, 2009
    • Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph and York Sure. Semantic Web: Grundlagen. Springer, 2007 (German)
    • Allemang and Hendler (2008): Semantic Web for the Working Ontologist. Verlag Morgan Kaufmann.
    • Antoniou and van Harmelen (2004): A Semantic Web Primer. MIT Press.
    • Heath and Bizer (2011): Linked Data: Evolving the Web into a Global Data Space. Free online version.
  • Course Evaluations

    Course evaluations from previous semesters: