2017-2018 / INFO8005-1

Semantic Data

Duration

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  

Lecturer

Jean-Louis Binot

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

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 striking 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, to provide a strong theoretical basis for the remaining content.

Semantic networks and ontologies will be introduced, and historical difficulties in reasoning with semantic networks discussed. After an introduction to formal logic, 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 (knowledge agents, ontologies, semantic networks, frames).
  • Introduction to formal logic.
  • The Semantic Web resource description framework (the initiative, RDF, RDFS, SPARQL).
  • Modern ontologies (types, uses, ontological commitment).
  • Introduction to description logics.
  • The Web Ontology Language: OWL.
  • Ontology Engineering.
  • Reasoning with description logics.
  • Ontology-based data access and Big Data.
  • Ontologies in software engineering.
  • Business application domains.

Learning outcomes of the learning unit

At the end of the course, the students will have gained a broad understanding of the thriving field of linked semantic data, including its theoretical foundations, application domains and related technologies.

They will know the knowledge representation principles and inference mechanisms related to ontologies and the logical formalisms supporting them.

They will also have learned the vision, ideas and formal languages of the semantic web, and understand what are 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 the 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: an introduction to logic sufficient 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;
  • case studies of modern developments in selected domains.
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 courses.
 
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, knowledge agents and formal logic:

  • Chapters 7, 8 and 12 of the book of Russel and Norvig: Artificial Intelligence: A Modern Approach (3rd Edition), Pearson, 2010, also used in the course INFO8006 Introduction to Artificial Intelligence.
  • Chapters 2 and 5 of the course INFO0051 Logic.
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 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 (closed books) written exam in June, and if required a second-session written exam in September.
 
The exam will be based on open questions and practical problems allowing to assess the level of understanding of the subject matter.
 
Project results will be assessed from a written report, 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% (report 5%, implementation 25%, defense 10%).
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 September 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 starts on 7/2/2018.
 
The project will begin during the fifth week of the course (to allow the students to acquire the necessary knowledge) and its results will be presented during the last session of the course mid-May 2018.

Contacts

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