Module CS4130-KP06, CS4130

Duration

1 Semester

Turnus of offer

each summer semester

Credit points

6

Course of studies, specific fields and terms:

• Master Computer Science 2019, compulsory, Canonical Specialization Data Science and AI
• Master Entrepreneurship in Digital Technologies 2020, basic module, Applied computer science
• Master Media Informatics 2020, optional subject, computer science
• Master Computer Science 2019, basic module, Applied computer science
• Master Medical Informatics 2019, basic module, Applied computer science
• Master Robotics and Autonomous Systems 2019, optional subject, Elective
• Master IT-Security 2019, basic module, Applied computer science
• Master Medical Informatics 2014, basic module, ehealth / infomatics
• Master Media Informatics 2014, optional subject, computer science
• Master Entrepreneurship in Digital Technologies 2014, basic module, Applied computer science
• Master Computer Science 2014, optional subject, specialization field software systems engineering
• Master Computer Science 2014, basic module, Applied computer science

Classes and lectures:

• Information Systems (exercise, 2 SWS)
• Information Systems (lecture, 2 SWS)

Workload:

• 60 hours in-classroom work
• 20 hours exam preparation
• 100 hours private studies

Contents of teaching:

• Motivation of knowledge graphs and their relationship to the Semantic Web
• Overview over the W3C Semantic Web family of languages
• Comparison between and the interaction of knowledge graphs and generative artificial intelligence such as large language models
• Graph Neural Networks and their applications for tasks of knowledge graphs

Qualification-goals/Competencies:

• Knowledge: Students acquire an overview of knowledge graphs and the Semantic Web as well as generative artificial intelligence such as large language models and graph neural networks.
• Skills: Students can assess the possibilities and limitations of knowledge graphs and the Semantic Web. They can estimate the consequences of the Semantic Web approach for data modeling, data administration and processing and for applications. They can develop Semantic Web applications. They can use generative artificial intelligence such as large language models and graph neural networks to solve tasks for and in addition to knowledge graphs. They can discuss open research questions in the area of knowledge graphs and the semantic web as well as in comparison to generative artificial intelligence and graph neural networks.
• Social skills and independence: Students work in groups to complete exercises and small projects. Students' independent practical work is encouraged through exercises, some of them directly on the computer.

Grading through:

• Written or oral exam as announced by the examiner

Responsible for this module:

• Prof. Dr. Sven Groppe

Teacher:

• Institute of Information Systems
• Prof. Dr. Sven Groppe

Literature:

• M. Kejriwal, C. Knoblock : Knowledge graphs MIT Press, 2021
• S. Groppe : Data Management and Query Processing in Semantic Web Databases Springer, 2011
• W. L. Hamilton : Graph Representation Learning. In Synthesis Lectures on Artificial Intelligence and Machine Learning Springer International Publishing, 2020 /li>
• D. Jurafsky, J. H. Martin : Speech and language processing Upper Saddle River, NJ: Pearson, 2008
• D. Foster : Generative deep learning Sebastopol, CA: O’Reilly Media, 2023

Language:

• German and English skills required

Notes:

Last Updated:

06.01.2025