Kies de Nederlandse taal
Course module: 202200022
Knowledge Representation and Reasoning & Data Quality
Course info
Course module202200022
Credits (ECTS)4.5
Course typeStudy Unit
Language of instructionEnglish
Contact persondr. M.B. van Riemsdijk
dr. L. Gatti
Examiner M. van Keulen
dr. M.B. van Riemsdijk
dr. M.B. van Riemsdijk
Contactperson for the course
dr. M.B. van Riemsdijk
Academic year2023
Starting block
RemarksPart of TCS elective module 8E. Minor students: register for the minor!
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
After this module part, students: 
  • have knowledge of several key KRR formalisms for modelling concepts, actions, and time;
  • can determine the truth of sentences with respect to a model (semantics) and perform basic reasoning tasks with the KRR formalisms;
  • can apply the KRR formalisms to model and reason about activities and situations from daily life;
  • can critically reflect on the suitability and usability of the KRR formalisms for the representation problem under consideration, and compare with and relate to other AI approaches for addressing the task to identify complementarities and advantages & disadvantages.
  • have knowledge of multiple facets of data quality and understand its impact on machine learning and organizations;
  • can apply several techniques for detecting data quality problems based on data exploration-driven, constraint-driven, and machine learning-driven approaches;
  • can apply several techniques for cleaning or otherwise improving data quality such as deletion, consistent query answering, database repairs, and probabilistic data integration;
  • develop their critical attitude towards data to the level of assuming that there will always be quality problems in their data and that they will be prone to start actively searching for them.
  • Knowledge Representation & Reasoning (2.5 EC): Going to higher levels in the data-information-knowledge-wisdom (DIKW) hierarchy with their associated reasoning methods.
  • Data Quality (2 EC): Understanding the multiple facets and possible impact of data quality problems, as well as how to detect and clean them, because poor data quality is the main threat to the robustness of DS&AI technology: “garbage in is garbage out.”
Knowledge-based approaches to AI are concerned with the explicit representation of knowledge, for example in rule-based form, accompanied by reasoning mechanisms to derive new information. In this course, we build on the fundamentals of logic-based AI as taught in MOD06 (TCS/Create) to study tailored knowledge representation formalisms targeted at representation and reasoning about specific types of information.

We will investigate techniques for representing concepts and their relations (conceptual modeling & reasoning through ontologies), allowing to model information about the world; techniques for representing an agent’s actions using pre- and post-conditions and how these can be used to construct a plan to get to a goal state; and techniques for representing temporal relations between states and actions, allowing to reason about the progression of the agent’s activities and the world over time.

B-CS students register via Osiris; others can contact
Minor students: please register in Osiris for the minor!
Assumed previous knowledge
It is a prerequisite to have made a serious attempt at module 202000991 Intelligent Interaction Design or 202001031 Intelligent Interaction Design CS/BIT. A ‘serious attempt’ is defined as having participated in at least one test, i.e., one need not have successfully completed the module, but the module expects that the content of one of these modules has been studied.
Previous knowledge can be gained by
All reading materials are accessible online either publicly or through the UT library.
Module 8E
Participating study
Bachelor Technical Computer Science
Participating study
Bachelor Business & IT
Participating study
Bachelor Creative Technology
Required materials
S. Russell and P. Norvig (2020). Artificial Intelligence. A Modern Approach (4th edition). Pearson. ISBN 9780134671932
Scientific papers and thesis on temporal modelling.
Chapters from: Shazia Sadiq ed. (2013). Handbook of data quality: research and practice. Heidelberg, Germany: Springer. ISBN 978-3-642-36256-9.
Chapters from: Carlo Batini, Monica Scannapieco (2016). Data and information quality. Cham, Switzerland: Springer International Publishing. ISBN 978-3-319-24104-3
Recommended materials
Instructional modes
Presence dutyYes

Presence dutyYes



Presence dutyYes

Project supervised

Project unsupervised


Knowledge Representation & Reasoning, Data Quality

Kies de Nederlandse taal