TKRR25 - Knowledge Representation and Reasoning

Course Code:	TKRR25
Confirmed:	Oct 15, 2024
Valid From:	Aug 31, 2026

Education Cycle:	Second-cycle level
Disciplinary domain:	Technology
Subject group:	Computer Technology
Specialised in:	A1N Second cycle, has only first-cycle course/s as entry requirements
Main field of study:	Computer Science

Intended Learning Outcomes (ILO)

On completion of the course the student shall:

Knowledge and understanding

show familiarity with the principles of knowledge representation and reasoning
demonstrate comprehension of the knowledge representation methods, such as description logics, ontologies, and semantic web technologies
display knowledge of current research in the various areas of knowledge representation, such as knowledge graphs, answer-set programming and reasoning about actions and change

Skills and abilities

demonstrate skills of modelling knowledge using different knowledge representation methods
demonstrate the ability to use established tools to work on knowledge representation and reasoning tasks

Judgement and approach

demonstrate the ability to choose applicable knowledge representation methods and tools for AI systems
demonstrate an understanding of the limitations of chosen knowledge representation methods and tools

Content

Knowledge representation and reasoning (KR) is an established area in Artificial Intelligence (AI) since the 1960s. It deals with explicit, symbolic, declarative representations of knowledge and inference procedures for deriving further and implicit information from these representations. It remains crucial for the overall development of AI. Although recent developments in statistical approaches and machine learning have been very successful, limitations of these methods are apparent. It is believed that some of these limitations can only be overcome with advances in knowledge representation. This course aims to introduce students to various topics in KR. The course covers both fundamental principles and recent advancements in the field, including those developed from the area of the Semantic Web, whose backbone is based on theories and methods built in knowledge representation.

The course includes the following elements:

Description Logics
Ontology and Ontology Engineering
Knowledge Graphs
Standard Semantic Web technologies
Answer-Set Programming and reasoning about actions and change

Type of instruction

Instruction will consist of lectures and practical exercises.

Language of instruction is English.

Entry requirements

The applicant must hold the minimum of a bachelor’s degree (i.e the equivalent of 180 ECTS credits at an accredited university) with at least 90 credits in Computer Engineering, Computer Science or Electrical Engineering (with relevant courses in computer engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering. The bachelor’s degree should comprise a minimum of 15 credits in mathematics. Proof of English proficiency is required.

Examination and grades

The course is graded 5, 4, 3 or U.

Registration of examination:

Name of the Test	Value	Grading
Laboratory	5 credits	G/U
Project ¹	2.5 credits	5/4/3/U

¹Determines the final grade of the course, which is issued only when all course units have been passed.

Course literature

Please note that changes may be made to the reading list up until eight weeks before the start of the course.

Allemang, D., & Hendler, J. (2011). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Elsevier Science & Technology.

Rudolph, S. (2011). Foundations of description logics. In Reasoning Web International Summer School (pp. 76-136). Springer, Berlin, Heidelberg.

There may be additional academic articles; these are normally accessible online through open access-sources, or through the university library.

COURSE SYLLABUS