This course focuses on the methods and techniques of Semantic AI and their applications to support Knowledge Management (KM) tasks and systems. The course will cover applications, methods and techniques related to Semantic AI, which include, but not limited to:

• Ontology validation and quality improvements
• Ontology population from structured and unstructured data
• Machine Learning techniques to support Knowledge Engineering
• Knowledge Graph Question Answering (KGQA) with Large Language Models
• Semantics for Trustworthy AI

This course enables the participants to learn and apply Semantic AI methods and tools for knowledge management-related tasks. After successful completion of the course, students will be able to:

• Explain the value propositions of Semantic AI for Knowledge Management.
• Understand the concepts, apply methods, and utilise tools from Semantic AI on selected tasks and applications.
• Provide insights into the recent trends in Semantic AI and its application in academic and industrial applications.

Attendance is mandatory, with at least 80% of the hours attended, as per WU requirements regarding PI courses. The absences can be compensated in cases of illness with a doctor's note.  

This course builds on lectures, discussions, hands-on exercises, quizzes, assignments and student presentations. Teaching methods will include:

• Research-based teaching relying on the latest research advances in the area
• Practical experience on selected AI methods and tools
• Invited talks from companies that actively conduct research/and or build their products with Semantic AI

Components

• 40% written exam
• 40% group assignment
• 20% group presentation

Grade

• <60% (5)
• 60% - 69% (4)
• 70% - 79% (3)
• 80% - 89% (2)
• 90% - 100% (1)

Positive completion of courses 1 and 2 of the “Knowledge Management” SBWL. 
 

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Participants are expected to be familiar with basic Semantic Web technologies concepts and standards, such as ontologies, RDF/S, OWL, and SPARQL. 

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In line with Open Science principles, information artifacts created as part of course assignments may be utilized for research purposes following anonymization.