TH Köln

Master Digital Sciences

Documents for Study Program Accreditation

Module »Linked-Open Data and Knowledge Graphs« (LOD)

Organizational Details

Responsible for the module
Prof. Dr. Konrad Förstner (Faculty F03)
Offered in
Winter Semester (Duration 1 Semester)
Campus Köln Süd, or remote
Number of participants
minimum 6, maximum 20
Basic Python programming skills
Total effort 180h
Total contact time
60h (30h lecture / 15h exercise / 15h project supervision)
Time for self-learning
120h (containing 120h self-organized project work)
Project (during semester)
Competences taught by the module
Implement Concepts, Deploy Products, Optimize Systems, Apply Standardization
General criteria covered by the module
Interdisciplinarity, Digitization

Mapping to Focus Areas

Below, you find the module's mapping to the study program's focus areas. This is done as a contribution to all relevant focus areas (in ECTS, and content-wise). This is also relevant for setting the module in relation to other modules, and tells to what extent the module might be part of other study programs.

Focus Area ECTS (prop.) Module Contribution to Focus Area
Generating and Accessing Knowledge 4

The course introduces the basic concepts of Linked Open Data (LOD) as well as the construction of knowledge graphs.

Acting Responsibly 1

The course introduces the basic concepts of Linked Open Data (LOD) as well as the construction of knowledge graphs.

Designing Innovations and Products 1

The course introduces the basic concepts of Linked Open Data (LOD) as well as the construction of knowledge graphs.

Learning Outcome

The course aims to equip students with the knowledge and skills needed to leverage Linked Open Data (LOD) and Knowledge Graphs effectively in various professional contexts. Through active participation, discussions, and a hands-on project, students will gain a deep understanding of how LOD and knowledge graphs are transforming the way data is managed and knowledge is represented in the digital age. We will explore real world applications like Wikidata and Open Research Knowledge Graph (ORKG).

After finishing this course students are able to use, extend and construct knowledge graphs.

Module Content

  1. Linked Open Data
  2. Knowledge Graphs
  3. Semantic Searches
  4. RDF
  6. Applications - Wikidata and ORKG

Forms of Teaching and Learning

The course adopts an interactive seminaristic style, fostering active engagement and collaborative learning among participants. In addition to comprehensive lectures, the seminar incorporates paper discussions, enabling students to critically analyze and debate research papers and case studies related to Linked Open Data and Knowledge Graphs. Furthermore, students will have the opportunity to showcase their understanding through presentations, where they can articulate their insights and findings on relevant topics. To reinforce practical application, the seminar culminates in a programming project, where participants will design, implement, and evaluate a real-world project that involves the construction and querying of a knowledge graph, applying the principles learned throughout the seminar. This multifaceted approach ensures that students not only acquire theoretical knowledge but also gain hands-on experience and the ability to apply these concepts in practical scenarios.

Learning Material Provided by Lecturer

  • lecture slides and videos
  • exercises


  • Hitzler, Pascal. “A review of the semantic web field.” Communications of the ACM 64.2 (2021): 76-83.

  • “Knowledge Graphs – Methodology, Tools and Selected Use Cases”, Fensel, D., Şimşek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., Wahler, A., 2020, ISBN 978-3-030-37439-6