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.
Exam Review
The exam review for HWS 2023 will take place on Thursday, February 29th, at 10 am, in room B6 27-29 C101. In case you want to review your exam, please register for the exam review with Bianca Lermer.
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: Heiko Paulheim
- Practical Exercise: Sven Hertling
Dates
- Lecture: Tuesday, 15:30 – 17:00, B6 26, room A1.01
- Exercise: Friday, 12.00 – 13.30, A5 6, room A5 C015 <- changed!
Schedule
| Week | Tuesday | Friday |
| 04.09.2023 | Lecture: Introduction | Exercise: Introduction |
| 11.09.2023 | Lecture: Representing Knowledge in Graphs (RDF) | Exercise: Representing Knowledge in Graphs (RDF) |
| 18.09.2023 | Lecture: Lighweight Knowledge Graph Inference (RDFS) | Exercise: Lighweight Knowledge Graph Inference (RDFS) |
| 25.09.2023 | Lecture: Linked Data, Knowledge Graph Programming | Exercise: Linked Data, Knowledge Graph Programming, Kick Off Group Projects |
| 02.10.2023 | Holiday | No exercise, time to work on group projects |
| 09.10.2023 | Lecture: Querying Knowledge Graphs | Exercise: Querying Knowledge Graphs |
| 16.10.2023 | Lecture: Public Knowledge Graphs | Exercise: Public Knowledge Graphs |
| 23.10.2023 | Lecture: Labeled Property Graphs | Exercise: Labeled Property Graphs |
30.10.2023 | Lecture: Advanced Knowledge Graph Inference (OWL Part 1) | Exercise: Advanced Knowledge Graph Inference (OWL Part 1) |
| 06.11.2023 | No lecture (time to work on projects) | No exercise (time to work on projects) |
| 13.11.2023 | Lecture: Advanced Knowledge Graph Inference (OWL Part 2) | Exercise: Advanced Knowledge Graph Inference (OWL Part 2) |
| 20.11.2023 | Lecture: Knowledge Modeling | Exercise: Knowledge Modeling |
| 27.11.2023 | Lecture: Knowledge Graph Quality and Knowledge Integration | Exercise: Knowledge Graph Quality and Knowledge Integration |
| 04.12.2023 | Project Presentations |
Important dates for the group projects:
- Sunday, October, 8th, 23:59: Submission of project proposals
- Sunday, December 10th, 23:59: Submission of final reports
Adminstrative Details
Materials and Exercise Sheets
Material
Lecture Slides:
05.09.: Organization & Introduction (PDF, 14 MB)
12.09.: RDF (PDF, 5 MB)
19.09.: RDFS (PDF, 6 MB)
26.09.: Linked Open Data (PDF, 8 MB)
10.10.: SPARQL (PDF, 1 MB)
17.10.: Public Knowledge Graphs (PDF, 18 MB)
24.10.: Property Graphs (PDF, 4 MB)
31.10.: Advanced Knowledge Graph Inference (OWL Part 1) (PDF, 10 MB)
14.11.: Advanced Knowledge Graph Inference (OWL Part 2) (PDF, 3 MB)
21.11.: Knowledge Modeling (PDF, 5 MB)
28.11.: Data Quality and Linking (PDF, 17 MB)
Exercise:
08.09.: XML (PDF, 721 kB)
15.09.: RDF (PDF, 641 kB)
22:09:. RDFS (PDF, 224 kB)
29.09.: LOD (PDF, 496 kB) + Project (PDF, 534 kB)
13.10.: SPARQL (PDF, 223 kB)
20.10.: Public Knowledge Graphs (PDF, 83 kB)
27.10.: Property Graphs (PDF, 81 kB)
03.11.: OWL (Part 1) (PDF, 185 kB)
17.11.: OWL (Part 2) (PDF, 697 kB)
24.11.: Ontology Engineering (PDF, 191 kB)
01.12.: Interlinking (PDF, 134 kB)
Further material (slides, exercise solutions, videos, and other additional materials) will be made available in the corresponding ILIAS group.
Literature (suggested reading list):
- Aidan Hogan et al.: Knowledge Graphs. Morgan & Claypool, 2022, available online
- Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American, 284 (5), pp. 34–43, 2001
- Pascal Hitzler, Markus Krötzsch and Sebastian Rudolph. Foundations of Semantic Web Technologies. Chapman & Hall/
CRC, 2009 - Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph and York Sure. Semantic Web: Grundlagen. Springer, 2007 (German)
- Allemang and Hendler (2008): Semantic Web for the Working Ontologist. Verlag Morgan Kaufmann.
- Antoniou and van Harmelen (2004): A Semantic Web Primer. MIT Press.
- Heath and Bizer (2011): Linked Data: Evolving the Web into a Global Data Space. Free online version.
Course Evaluations
Course evaluations from previous semesters:
