91265 - Knowledge Engineering

Course Unit Page

Academic Year 2020/2021

Learning outcomes

At the end of the course, the student knows some (semi-)automated methods for joint interpretation of data and content as sources of knowledge. The student masters the basics of knowledge extraction, engineering, and linking, making data suitable to machine querying and automated reasoning, typically on decentralized platforms such as the Web.

Course contents

Knowledge graphs and ontologies


Ontology Design methodologies, focus on Extreme Design

Intensional and Extensional modelling

Ontology design patterns

Knowledge Acquisition

Applied reasoning


Notes and slides provided by the teacher.

Hitzler, P., Gangemi, A., & Janowicz, K. (2016). Ontology Engineering with Ontology Design Patterns. Amsterdam: IOS Press.

P.A. Bonatti, S. Decker, A. Polleres, V. Presutti, Knowledge graphs: new directions for knowledge representation on the Semantic Web (dagstuhl seminar 18371). Dagstuhl Rep. 8(9), 29–111 (2019)

Teaching methods

Lectures and self-assessment quizzes.

Assessment methods

  • Presentation (individual): critical analysis of a scientific paper selected from a list of proposals by the teachers 
  • Group project: selected from a list proposed by the teachers (with individual assessment)

Project: the students will apply the eXtreme Design methodology (learned and experimented during the classes) for creating a knowledge graph starting from an existing resource (database, text, etc.) by reusing / extending other ontologies

Teachers will assign a project to each group focusing on the Cultural Heritage domain and will provide the specification for its realisation. Before the assignment each group will have the possibility to express two preferences on a list of available projects. These preferences will be considered but not guaranteed (in case of conflicts).

Teaching tools

Slides and tools for self-assessment.

Office hours

See the website of Valentina Presutti

See the website of Andrea Giovanni Nuzzolese