Knowledge Representation on the Web


Course Objective

-Learn the requirements of representing knowledge at Web scale
(Knowledge and understanding)
-Master the RDF, RDFS, OWL and SPARQL languages, and their automatic
reasoning capabilities (Apply knowledge and understanding)
-Acquire dexterity in technologies that use these languages (NoSQL
databases, RDF libraries, query languages, ontology editors) (Knowledge
and understanding)
-Set up a project and apply these methods and technologies to build
fundamental Linked Data infrastructure (Apply knowledge and
understanding) (Make judgements)
-Learn to design experiments on top of the built infrastructure to make
a scientific contribution to these fields (Apply knowledge and
understanding) (Communication)
-Obtain familiarity with the research fields of Semantic Web, Knowledge
Representation, Information Extraction, and write a research paper about
the identified research problems (Apply knowledge and understanding)
(Communication) (Lifelong learning skills)

Course Content

In this course, you will learn the theory of knowledge representation
languages that are used to express information on the Web, their
application to real-world problems and data, and the research methods
behind them.

Teaching Methods

Content lectures (attendance mandatory)
Practical sessions (attendance mandatory)
Invited lectures (attendance mandatory)
Project development sessions
Demo market
Research paper writing

Method of Assessment

The exam (E): 30%
The project proposal (Pr) and Demo (D): 20% (10% each)
The final research paper (Pa): 50%


Handbook of Knowledge Representation (F. van Harmelen, V. Lifschitz, B.
A Semantic Web Primer (F. van Harmelen, G. Antoniou)
Linked Data: Evolving the Web into a Global Data Space (T. Heath, C.

Target Audience

MSc Artificial Intelligence

Explanation Canvas

All announcements and materials are made available via Canvas

Recommended background knowledge

Basic knowledge of programming; basic knowledge in logics; spoken and
written English. Basic knowledge in knowledge representation (SAT,
constraint programming, description logics, qualitative reasoning) a

General Information

Course Code XM_0060
Credits 6 EC
Period P5
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. A. Merono Penuela
Examiner dr. I. Tiddi
Teaching Staff dr. A. Merono Penuela
dr. I. Tiddi

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Practical
Target audiences

This course is also available as: