Knowledge Graphs (KGR)

Understand and Use Data and Knowledge as a Network: Learn how information is represented and structured as a network in knowledge graphs. The focus is on modeling relationships between data, integrating different data sources, and ensuring data quality. You will also develop simple knowledge graph applications and learn how to formulate queries and evaluate their results.

Contents

• Historical development of knowledge graphs and the Semantic Web
• Graph-based representation languages and formats: RDF and its serializations
• Query languages for knowledge graphs: SPARQL and Cypher
• Public knowledge graphs and Linked Open Data, in particular Wikidata
• Knowledge modeling: vocabularies and ontologies with RDFS, SKOS and OWL
• Property graph models and graph databases
• Construction of knowledge graphs: mapping, entity resolution and schema alignment
• Data quality, consistency and conflict resolution when integrating heterogeneous sources
• Knowledge graphs and machine learning: graph embeddings and link prediction
• Knowledge graphs and language models: structured knowledge as a complement to generative models, GraphRAG
• Application architectures and fields of use for knowledge graphs
• Practical exercises and project work to develop a knowledge graph application

Competencies

Students are able to

• explain the basic concepts, standards and architectures of knowledge graphs and the Semantic Web,
• choose suitable representation languages and modeling approaches for concrete use cases,
• design ontologies and model domain knowledge in knowledge graphs in a structured way,
• formulate queries on knowledge graphs and interpret the results,
• assess the provenance, licensing and quality of data sources,
• integrate various data sources and identify quality issues in knowledge graphs,
• combine knowledge graphs with language models and judge where structured knowledge compensates for the weaknesses of generative models,
• design, implement, deploy and evaluate a knowledge-graph-based application.

Literature

• A. Hogan et al., Knowledge Graphs. Cham, Switzerland: Springer, 2021. [Online]. Available: https://kgbook.org/ [Accessed: 19-Aug-2026].
• D. Allemang, J. Hendler, and F. Gandon, Semantic Web for the Working Ontologist: Effective Modeling for Linked Data, RDFS, and OWL, 3rd ed. New York, NY, USA: Association for Computing Machinery, 2020.
• B. DuCharme, Learning SPARQL: Querying and Updating with SPARQL 1.1, 2nd ed. Sebastopol, CA, USA: O’Reilly Media, 2013.
• J. Barrasa and J. Webber, Building Knowledge Graphs: A Practitioner’s Guide. Sebastopol, CA, USA: O’Reilly Media, 2023.
• M. Kejriwal, C. A. Knoblock, and P. Szekely, Knowledge Graphs: Fundamentals, Techniques, and Applications. Cambridge, MA, USA: The MIT Press, 2021.
• S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, and X. Wu, “Unifying Large Language Models and Knowledge Graphs: A Roadmap,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 3580–3599, 2024.

Lecturer

• Prof. Dr. Kai Eckert
• Tobias Malmsheimer

Recommended Previous Knowledge 

• Basic knowledge of databases and data models
• Basic knowledge in computer science and software development
• Basic knowledge in formal languages or logic (an advantage, but not strictly required)
• Experience in programming, we use Python for this lecture.

Module Details

Semester	6/7
Lecture Language	German
Frequency	Not regularly
Credit Points (ECTS)	5
Course Coordinator	Prof. Dr. Kai Eckert
Duration	1 Semester
Course Achievement	None
Prerequisite for exam	None
Exam	Continuous Assessment (CA)

Weekly Hours (SWS) 

Lecture	2 SWS
Exercises	2 SWS
Sum	4 SWS

Work Load 

Lecture	30 h
Lab	30 h
Self Study	30 h
Assignments	30 h
Project	30 h
Sum	150 h