This course focuses on the capabilities, methods and techniques of Semantic Artificial Intelligence (AI) and their applications to support Knowledge Management (KM) tasks and systems. Semantic AI denotes an emerging family of technologies that enjoy large-scale up-take in the industry. 

Firstly, it will cover the general description of Semantic AI capabilities relevant to the KM tasks and systems. Secondly, the course will cover applications, methods and techniques of Semantic AI in real-world use cases, which include: 

• Data Integration
• Search and knowledge discovery
• Knowledge analytics
• AI system auditability
   

After successful completion of the course, students understand the capabilities of Semantic AI technologies and apply these technologies to solve problems in Knowledge Management and other related disciplines. The course will strengthen the understanding of the existing applications of advanced Semantic AI approaches and furthermore allow a better evaluation of the practical applicability of such technologies for cases at hand. 

Furthermore, students will get familiar with the recent research developments in this field.

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, assignments and student presentations.

Teaching methods will include:

• Research-based teaching relying on the latest research advances in the area
• Invited talks from companies that base their business models on semantic technologies
• Use cases from real-world settings.

Components

• 20% individual assignment
• 40% group assignment (including presentation)
• 40% exam

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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