Syllabus

Title
0692 Applications of Semantic AI in Knowledge Management
Instructors
Dipl.-Ing. Laura Waltersdorfer, BSc
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/03/24 to 11/28/24
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Tuesday 12/03/24 09:00 AM - 12:00 PM TC.5.04
Thursday 12/05/24 09:00 AM - 12:00 PM TC.1.02
Tuesday 12/10/24 09:00 AM - 12:00 PM TC.4.16
Thursday 12/12/24 09:00 AM - 12:00 PM D2.0.392
Tuesday 12/17/24 12:00 PM - 03:00 PM TC.2.03
Tuesday 01/14/25 08:00 AM - 11:00 AM TC.3.05
Tuesday 01/21/25 09:00 AM - 01:30 PM TC.2.03
Contents

This course focuses on the capabilities, methods and techniques of Semantic Artificial Intelligence (Semantic AI) and their applications to support Knowledge Management (KM) tasks and systems. Semantic AI denotes an emerging family of Semantic Web technologies that enjoy large-scale up-take in the industry. The course will cover applications, methods and techniques of Semantic AI, which include, but not limited to:

  • Knowledge population from structured and unstructured data
  • Combination of Semantic AI and Large Language Models for Knowledge Management
  • Semantic AI for trustworthy Knowledge Management systems
  • Recent trends of Semantic AI research and applications
Learning outcomes

This course enables the participants to learn and apply semantic AI methods and tools in Knowledge Management. After successful completion of the course, students will be able to: 

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

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

Attendance requirements

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.  

Teaching/learning method(s)

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 Semantic AI methods and tools
  • Invited talks from companies that base their business models on semantic technologies
Assessment

Components

  • 30% group assignment
  • 30% group assignment presentation
  • 40% written exam

Grade

  • <60% (5)
  • 60% - 69% (4)
  • 70% - 79% (3)
  • 80% - 89% (2)
  • 90% - 100% (1)
Prerequisites for participation and waiting lists

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

Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Recommended previous knowledge and skills

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

Availability of lecturer(s)

via Email

Open Science

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

Last edited: 2024-10-01



Back