Admission Requirements
The course is available as elective for master students in Artificial Intelligence and similar programmes of Maastricht University. The background in computer science is desired prior knowledge.

Learning Outcomes
Competencies:
- constructing knowledge graphs and interpreting information
- Taking into account the size and complexity of knowledge graphs, combine and interpret information across different topics
Knowledge:
- Concept of knowledge graphs
- Overview of methods to mine information using knowledge graphs
Skills:
- Use of machine learning, graph databases, ontologies, automated reasoning, and other techniques for data mining and knowledge representation

Programme
Approaches to construct and use knowledge graphs across a diverse set of applications using cutting-edge technologies such as machine learning and deep learning, graph databases, ontologies and automated reasoning, and other relevant techniques in the area of data mining and knowledge representation

References
Aggarwal, C.C. and Wang, H. eds., (2010) Managing and mining graph data (Vol. 40). New York: Springer. ISBN 978-1-4419-6045-0

Teaching Methodology
Project-centered learning

Language of the Curriculum and Course
English

ECTS Credits
The course lasts 2 months and includes 6 ECTS

Examination Methodology
Assignment, assessment

Relevance
In the process of modelling or analysing complex systems, the information about constituents of the system is difficult to understand due to their complex, usually nonlinear relationship. Knowledge graphs enable to represent, explore, understand, and reuse information and the relationship of entities, especially in the analysis across different topics. The applications of knowledge graphs include answering questions, finding relevant content, understanding social structures, and making scientific discoveries. Understanding relationships of parts and entities of complex system enable to create sustainable solutions.