SKGs Compact Course: Semantic Knowledge Graphs for Reasoning and Linked Data

• Date: 14.01.2027
• Time: 9am - 1pm
• Instructor: Dr. Inga Ulusoy, Research Software Engineer, Scientific Software Center
• Venue: Mathematikon Bauteil A, Im Neuenheimer Feld 205, in the conference room 5/104 on the 5th floor

This is a half-day course.

Participants should have basic Python experience, shell commands and git, as well as some basic familiarity with structured data and databases. Participants should be comfortable working with formats such as CSV or JSON and have a basic understanding of concepts such as identifiers, metadata, and relationships between data entities. No prior knowledge of RDF, ontologies, SPARQL, or knowledge graphs is required. Participants are required to bring their own laptops to work on during the course. Network access (e.g. through Eduroam) is necessary.

Knowledge graphs, i.e., representations of knowledge in machine-readable graph form, provide a powerful way to represent and connect heterogeneous data. Using shared vocabularies, relationships between entities become standardized, explicit, and allow a more meaningful data integration and querying. Participants will learn the differences between conventional graphs, knowledge graphs, and semantic knowledge graphs. We will explore how ontologies and shared vocabularies provide a semantic layer for describing entities and their relationships. During the course, we will build semantic knowledge graphs from relational data using adapter code and the support of AI agents. The connection of conceptual foundations with practical implementation will illustrate how ontologies, adapters, and knowledge graphs can support interoperability, integration, and reuse of heterogeneous research data. Some use case examples in modern AI technology will be provided.

After the course participants will be able to

• Understand concepts of knowledge graphs, semantic knowledge graphs, linked data, ontologies, and their relations
• Identify and model entities and relationships, as well as semantic concepts
• Use ontologies and controlled vocabularies to provide shared, machine-interpretable meaning to data
• Transform relational data into a knowledge graph, and develop or use data adapters to extract, transform, and map relational data according to an ontology
• Assess the benefits and limitations of semantic knowledge graphs for integrating and reusing research data and different use cases

Sign up for this course here: Signup for the next scheduled course