2018-2019 / INFO8005-1

Semantic Data


25h Th, 10h Pr, 45h Proj.

Number of credits

 Master in data science (120 ECTS)5 crédits 
 Master of science in computer science and engineering (120 ECTS)5 crédits 
 Master in data science and engineering (120 ECTS)5 crédits 
 Master in computer science (120 ECTS)5 crédits 


Jean-Louis Binot

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

This course aims to provide an overview of the field of linked semantic data, which has seen strong progress in recent years and has become a key domain bridging several modern IT areas, including artificial intelligence, advanced web initiatives, big data solutions, and software engineering.

The course will first cover the conceptual foundations of the representation of semantic knowledge and its use for inferences.  Semantic networks and ontologies will be introduced, and historical difficulties in reasoning with semantic networks discussed. Description logics will be presented as theoretical basis for ontology-based reasoning, with appropriate semantics and inference algorithms.

The course will show how these concepts are reused by the semantic web initiative, the purpose of which is to enrich the web with linked semantic data to make it more usable by machines. The link between description logics and the ontology web language OWL will be further developed.

Finally, the course will illustrate how semantic data are used in modern industrial areas.

The main topics covered will be:

  • Knowledge representation foundations.
  • The Semantic Web resource description framework (the initiative, RDF, RDFS, SPARQL).
  • Modern ontologies (types, uses, ontological commitment).
  • Introduction to description logics (including a summary of useful first order logic concepts).
  • The Web Ontology Language: OWL.
  • Ontology Engineering.
  • Reasoning with description logics.
  • Ontology-based data access.
  • Selected business application domains (bioinformatics, software engineering, energy ...) and case studies of modern developments in some of these domains.
A specific seminar on IT data management in industrial contexts will be given as introduction for the case studies; this seminar is also part of the course content.

Learning outcomes of the learning unit

At the end of the course, the students will have gained a broad understanding of the field of semantic data, including its theoretical foundations, application domains and related technologies.
They will have learned the knowledge representation principles and inference mechanisms related to ontologies and the logical formalisms supporting them.
They will also understand the vision, ideas and formal languages of the semantic web, as well as the emerging technologies, leading-edge application areas and some of the open questions related to this field.
Finally, they will have developed a practical experience of the tools and challenges involved in building and using an ontology-based application for a specific domain.

Prerequisite knowledge and skills

There are no prerequisite courses required.

A previous experience of formal logic is useful but not necessary: a summary of the concepts of first order logic necessary to understand the material of the course is provided.

For the project, a programming experience sufficient to understand or to learn XML-based standards, the basics of Java and the use of a Java-based API (application programming interface) is expected.

Planned learning activities and teaching methods

Lectures (25 h) will cover the theoretical content of the course.

Practice sessions (10h) will cover:

  • exercises on the theoretical foundations;
  • an introduction to ontology modeling and to the programming tools needed for the project.
A project will be realized in small groups (2 - 3 people) with the goal to implement an ontology for a specific domain and to access this ontology to retrieve information, using open source tools.

Mode of delivery (face-to-face ; distance-learning)

The theoretical sessions and practice sessions will be delivered face-to-face.
The project will mostly be carried out remotely. A final review with presentation and defense of results will take place during the last session of the course.

Recommended or required readings

The reference material for the theory and the practice sessions is provided in the slides of the course.
Due to the diversity of the subject matter there is no global reference textbook. Useful complementary readings will be indicated at the start of each chapter when relevant.
The following sources provide non-required but useful reading on knowledge representation and description logics:

  • Chapters 7, 8 and 12 of the book of Russel and Norvig: Artificial Intelligence: A Modern Approach (3rd Edition), Pearson, 2010.
  • Chapters 1, 2, 4, 7 and 8 of the book of Baader, Horrocks, Lutz and Sattler: An introduction to description logic, Cambridge University Press, 2017.
If specific papers are used for case studies, they are considered as required reference material; they will be indicated at the start of the corresponding chapter. The material of the seminar on IT data management is also part of the required material.
The realization of the project will require to consult the online documentation of the semantic web standards and the chosen tools. Appropriate pointers will be provided.

Assessment methods and criteria

Content of the theory and exercises sessions will be assessed by an individual oral exam in June, and if required a second-session oral exam in August/September.

The exams will take place with closed books and will be based on open questions, both theoretical and practical, allowing to assess the level of understanding of the subject matter.
Project results will be assessed from the resulting implementation, and a final defense, including a presentation and a demonstration.
Timely submission of project results is mandatory for presenting the exam!
The grade allocation will be split as follows:

  • written exam: weight 60%,
  • project results: weight 40% (implementation 25%, defense 15%).
Grades for the project are normally assigned to the whole group.  However, in some cases (e.g. when there is evidence that a member of a group has not participated enough in the project), the grade may be assigned individually, reflecting the personal involvement of each member of a group.
Students who must represent the course in the August/September session may opt either to keep the grade obtained for the project in June or to improve their project. This improvement will have to be carried out individually if no member of the same project group is in the same situation. There is no support guaranteed during the summer for project improvement.

Work placement(s)

Non applicable.

Organizational remarks

The course takes place in second semester; the exact start date and location will be posted on the course web page.

The project will begin a few weeks after the start of the course (to allow the students to acquire the necessary knowledge); the exact start date will be posted on the course web page. The project results will be presented during one of the last sessions of the course.


Lecturer: Jean-Louis Binot (jean-louis.binot@uliege.be).

Items online

Semantic Data web page

This web page will contain the material for the course, including theory, practice and project.