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/
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.
Prerequisites
- 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.
Lecturers
- Lecture: Dr. Sven Hertling
- Practical Exercise: Dr. Rita Sousa
Dates
- Lecture: Tuesday, 15:30 – 17:00, Room D007 (2) Building B6,27 Part D (in the backyard of B6, 23 Part A)
- Exercise: Friday, 12.00 – 13.30, Room D007 (2) Building B6,27 Part D (in the backyard of B6, 23 Part A)
Schedule and Materials
Week | Tuesday | Friday |
02.09.2024 | No lecture | No Exercise |
09.09.2024 | Lecture: Introduction | Exercise: Introduction |
16.09.2024 | Lecture: Representing Knowledge in Graphs (RDF) | Exercise: Representing Knowledge in Graphs (RDF) |
23.09.2024 | Lecture: Lighweight Knowledge Graph Inference (RDFS) | Exercise: Lighweight Knowledge Graph Inference (RDFS) |
30.09.2024 | Lecture: Linked Data, Knowledge Graph Programming, | Exercise: Linked Data, Knowledge Graph Programming |
07.10.2024 | Lecture: Querying Knowledge Graphs | Exercise: Querying Knowledge Graphs |
14.10.2024 | Lecture: Public Knowledge Graphs | Exercise: Public Knowledge Graphs |
21.10.2024 | Lecture: Labeled Property Graphs | Exercise: Labeled Property Graphs |
28.10.2024 | Lecture: Advanced Knowledge Graph Inference (OWL Part 1) | Public Holiday |
04.11.2024 | Lecture: Advanced Knowledge Graph Inference (OWL Part 2) | Exercise: Advanced Knowledge Graph Inference (OWL Part 1) |
11.11.2024 | No lecture (time to work on projects) | Exercise: Advanced Knowledge Graph Inference (OWL Part 2) |
18.11.2024 | Lecture: Knowledge Modeling | Exercise: Knowledge Modeling |
25.11.2024 | Lecture: Knowledge Graph Quality and Knowledge Integration | Exercise: Knowledge Graph Quality and Knowledge Integration |
02.12.2024 | Project Presentations | Exercise: Q & A |
Important dates for the group projects:
- Sunday, October, 20th, 23:59: Submission of project proposals
- Sunday, December 8th, 23:59: Submission of final reports