Modul CS4130-KP06, CS4130

Duration:

Turnus of offer:

Course of studies, specific field and terms:

• Master Computer Science 2019 (compulsory), Canonical Specialization Data Science and AI, Arbitrary semester
• Master Entrepreneurship in Digital Technologies 2020 (basic module), technology field computer science, 1st or 2nd semester
• Master Media Informatics 2020 (optional subject), computer science, Arbitrary semester
• Master Computer Science 2019 (basic module), Applied computer science, 1st or 2nd semester
• Master Medical Informatics 2019 (basic module), Applied computer science, 1st or 2nd semester
• Master Robotics and Autonomous Systems 2019 (optional subject), Elective, 1st or 2nd semester
• Master IT-Security 2019 (basic module), Applied computer science, 1st or 2nd semester
• Master Medical Informatics 2014 (basic module), ehealth / infomatics, 1st or 2nd semester
• Master Media Informatics 2014 (optional subject), computer science, Arbitrary semester
• Master Entrepreneurship in Digital Technologies 2014 (basic module), technology field computer science, 1st or 2nd semester
• Master Computer Science 2014 (optional subject), specialization field software systems engineering, 2nd or 3rd semester
• Master Computer Science 2014 (basic module), Applied computer science, 1st or 2nd semester

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

Teachers:

• 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
• 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:

Admission requirements for taking the module:
- None

Admission requirements for participation in module examination(s):
- Successful completion of exercises as specified at the beginning of the semester

Module Exam(s):
- CS4130-L1: Information Systems, written exam or oral exam, 100% of module grade

Previous name: Web Based Information Systems

Letzte Änderung: