This course focuses on how techniques from semantic Artificial Intelligence (AI) can provide a technological foundation for enabling Knowledge Management (KM) tasks and processes. Semantic Artificial Intelligence denotes an emerging family of technologies which currently enjoy large-scale up-take in the industry. After a broad introduction of AI techniques for KM, the course will focus on semantic based AI techniques. Firstly, it will cover the basics of how (expert) knowledge can be captured in information artifacts such as taxonomies, ontologies and knowledge graphs. Secondly, the course will introduce methods to build such information artifacts from implicit knowledge (from employees) and explicit knowledge residing in data and documents in an enterprise. Thirdly, the course will also cover topics related to storing and querying such novel knowledge structures.

This course enables the participants to learn and apply fundamental techniques of semantic AI. Participants will be able to:

• explain how Artificial Intelligence techniques in general can support  Knowledge Management
• clearly identify various knowledge representation technologies (taxonomies, ontologies, knowledge graphs) and understand differences between them
• apply ontology engineering methods for capturing implicit knowledge from experts through ontology engineering
• use methods for reasoning over explicit knowledge
• apply methods and use tools for querying knowledge structures

After completing this course,  participants will be able to reliably understand and practice a number of core methods and tools relevant for these technologies.

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 the doctor's note.  

This course builds on lectures, discussions, class exercises, in-class quizzes and individual/group assignments.

In line with Open Science principles, information artifacts created as part of course assignments and in-class exercises, as well as student performance and results achieved during the course, may be analysed and used for research purposes following anonymisation.

Graded components:

• 50p Exam [ >= 25p needed for a positive grade]
• 30p Individual Assignment 
• 20p In-class exercises  

Bonus: 

• 4p participation in research study
• 2p quizzes

Grading scale:

• < 60 points or < 25 points on the exam: 5 (Fail)
• 60 ≤ points < 70: 4
• 70 ≤ points < 80: 3
• 80 ≤ points < 90: 2
• 90 ≤ points: 1

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In line with Open Science principles, information artifacts created as part of course assignments and in-class exercises, as well as student performance and results achieved during the course, may be analysed and used for research purposes following anonymisation.