392101 Knowledge Representation (S) (SoSe 2026)

Contents, comment

The course covers foundational concepts and methods in Knowledge Representation and Reasoning (KR), addressing both theoretical foundations and practical applications. It focuses on symbolic and sub-symbolic approaches to representing, structuring, and reasoning over knowledge in intelligent systems.

Core topics include:

- First-Order Logic (FOL)

- Logic Programming and Automated Reasoning (Prolog programming, Rule-based reasoning)

- Structured Knowledge Representation (Frames, slots, and inheritance hierarchies)

- Start into Semantic networks

- Resource Description Framework (RDF)

- Knowledge graphs Reasoning

The second part of the course focuses on Knowledge Graph including:

- Knowledge acquisition from structured and unstructured data

- Distributional semantics and word embeddings

- Knowledge graph embeddings

- Graph Neural Networks (GNNs) for knowledge representation

Advanced application topics include:

- Knowledge tracing

- Parametric knowledge in large language models

- Knowledge-graph-enhanced recommender systems

Requirements for participation, required level

Useful prior knowledge: Discrete Mathematics, Formal Logic, Introduction to Machine Learning

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
weekly Di 14-16 X-E0-205 13.04.-24.07.2026
not on: 4/21/26 / 5/5/26
einmalig am 05.05.2026 in H9

Show passed dates >>

Subject assignments

Module Course Requirements  
39-Inf-WP-DS Data Science (Basis) Data Science (Basis) Einführendes Seminar Student information
- Graded examination Student information
39-Inf-WP-DS-x Data Science (Focus) Data Science (Schwerpunkt) Einführende Veranstaltung Seminar o. Vorlesung Student information
- Graded examination Student information
39-Inf-WP-KI Artificial Intelligence (Basis) Künstliche Intelligenz (Basis) Einführendes Seminar Student information
- Graded examination Student information
39-Inf-WP-KI-x Artificial Intelligence (Focus) Künstliche Intelligenz (Schwerpunkt) Einführende Veranstaltung Seminar o. Vorlesung Student information
- Graded examination Student information
39-M-Inf-AI-bas Basics of Artificial Intelligence Basics of Artificial Intelligence Basics of Artificial Intelligence: Seminar Student information
- Ungraded examination Student information

The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.


No more requirements
Moodle Courses
Moodle Courses
Registered number: 30
This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
eKVV participant management:
eKVV participant management is used for this course.
Show details
Address:
SS2026_392101@ekvv.uni-bielefeld.de
This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_654011536@ekvv.uni-bielefeld.de
Notes:
Additional notes on the electronic mailing lists
Email archive
Number of entries 1
Open email archive
Last update basic details/teaching staff:
Monday, April 20, 2026 
Last update times:
Monday, May 4, 2026 
Last update rooms:
Monday, May 4, 2026 
Type(s) / SWS (hours per week per semester)
seminar (S) / 2
Language
This lecture is taught in english
Department
Faculty of Technology
Questions or corrections?
Questions or correction requests for this course?
Planning support
Clashing dates for this course
Links to this course
If you want to set links to this course page, please use one of the following links. Do not use the link shown in your browser!
The following link includes the course ID and is always unique:
https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=654011536
Send page to mobile
Click to open QR code
Scan QR code: Enlarge QR code
ID
654011536