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 capturing explicit knowledge from structured data and unstructured/textual sources;
• apply methods and use tools for storing and 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.

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 the doctor's note.  

This course builds on lectures, discussions, class exercises, individual assignments and student presentations. Teaching methods will include:

• research-based teaching relying on the latest research advances in the area
• use-cases from real-life settings.

40p Individual/Group Assignment (dependant on number of participants)

40p Exam

20p End-term presentation of the assignment

Grading scale:

<60p (5)

60p - 69p (4)

70p - 79p (3)

80p - 89p (2)

90p - 100p (1)

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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.