Duration
24h Th, 20h Pr, 45h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course provides an overview of the knowledge representation and reasoning applied to knowledge graphs and Linked Data. Topics include graph data models, modeling languages, reasoning, graph query languages, and declarative approaches to semantically integrating heterogeneous data sources. The following topics will be covered:
- Introduction to Ontologies, Knowledge Graphs, and Open Information Systems
- The RDF graph data model
- KR and Reasoning: RDF(S)
- KR and Reasoning: Description Logics
- KR and Reasoning: Web Ontology Language
- Querying RDF
- Knowledge Graph Construction with R2RML and RML
- Rule-based Reasoning
- Data modeling and validation with SHACL
- Evaluating and validating ontologies and knowledge graphs
- Linked Data and its role in enterprises
- Metadata and provenance information
Learning outcomes of the learning unit
After passing this course, the student understands the main concepts of knowledge graphs, semantics, meaningful and declarative data integration, and how Linked Data and knowledge graph technologies enable semantic interoperability (within and between organizations).
The student can understand and evaluate developments and technologies in the field of knowledge representation and reasoning (both existing and new) and will be able to independently study and master them.
The student can integrate information from various sources and build applications using Linked Data and knowledge graph technologies.
The student can make informed decisions on how to best represent and use information with Linked Data and knowledge graph technologies. The student can express those both in written and oral form.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.2, IV.1, VI.1, VI.2, VII.1, VII.3, VII.4, VII.5 of the MSc in computer science and engineering.
Prerequisite knowledge and skills
While there are no prerequisite courses required, the following skills will be useful:
- Some programming experience
- Some modeling experience (ER, ERD, UML, ORM,...)
Planned learning activities and teaching methods
- Ex-cathedra lectures given by the lecturer will cover various topics.
- The lab sessions will be used to gain hands-on experience on the material via small exercises (mostly at the beginning of the semester and at the beginning of a session) and discuss (in a classroom setting) the project.
- In groups of three, students will carry out a project in which they will need to apply the material covered in class.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
Ex-cathedra courses and some key practicals are (or will be) made available as recordings. Course material will be made available on eCampus. The eCampus environment will furthermore be used for communication.
Recommended or required readings
The slides supporting the ex-cathedra lessons cover most of the course. The slides are thus mandatory.
Highly recommended are the links to open standards (specifications) and papers shared on eCampus. Consulting those will be necessary to gain a deeper understanding of topics and deliver the project.
Recommended reading:
Staab, S. and Studer, R. (eds.): Handbook on Ontologies (2nd edition), Springer, 2009.
- This handbook approaches some topics from a practical point of view and contains useful exercises.
- This book covers topics such as ontology evaluation and Description Logics. This book may be useful for students exploring these aspects in their projects.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Other : Presentation
Additional information:
- Project: software artifacts and project report (group) 50%
- Project: presentation and defense of the project (group) 10%
- Oral exam (individual) 40%
For the project's presentation, students will be awarded a grade for the overall presentation which will be adjusted to each student's performance which can deviate at most 2 points from that grade (unless a student clearly underperforms or outperforms during the presentation). Students will evaluate each other's contributions via peer assessment.
The oral exam will constitute both questions on the course material as well as the project.
The same modalities for the second session hold. Students who have not obtained a 10/20 for the project and/or presentation may resubmit revised deliverables.
The project and presentation are mandatory learning activities. Students not taking part in these activities will obtain an absence grade (A) for the course.
Work placement(s)
Organisational remarks and main changes to the course
The course is organized in English. All material will be made available on eCampus. This includes the project description with information on deadlines, milestones, etc.
Contacts
- Lecturer: Christophe Debruyne