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91265 - Knowledge Engineering

Academic Year 2021/2022

                
                        Docente:
                        Valentina Presutti
                    
                        Credits:
                        6
                    
                        SSD:
                        INF/01
                    
                        Language:
                        English
                    
                        Moduli:
                        
                            Valentina Presutti
                            (Modulo 1)
                        
                            Andrea Giovanni Nuzzolese
                            (Modulo 2)
                        
                        Teaching Mode:
                        
                                    Traditional lectures (Modulo 1)
                                
                                    Traditional lectures (Modulo 2)
                                
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Artificial Intelligence (cod. 9063)

                            Teaching resources on Virtuale

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

OWL, RDF, SPARQL

Ontology Design methodologies, focus on Extreme Design

Intensional and Extensional modelling

Ontology design patterns

Applied reasoning

Knowledge extraction from text

Ontology evaluation and quality

Readings/Bibliography

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)

Aidan Hogan et. al. Knowledge graphs. ACM Computing Surveys, Vol. 54, No. 4, Article 71

Semantic Web (W3C Recommendations):

OWL 2: https://www.w3.org/TR/2012/REC-owl2-rdf-based-semantics-20121211/

RDF: https://www.w3.org/TR/2014/REC-rdf11-mt-20140225/

SPARQL: https://www.w3.org/TR/2013/REC-sparql11-query-20130321

Teaching methods

Lectures, lab practice, and self-assessment quizzes.

Assessment methods

Group project: selected from a list proposed by the teachers (with individual assessment)
Presentation (individual): critical analysis of a scientific paper (15 minutes)

Project: the students will apply the eXtreme Design methodology (learned and experimented during the classes) for creating a knowledge graph starting from existing resources (databases, texts, etc.) by reusing / extending other ontologies and when necessary by applying tools and methods for knowledge extraction, entity linking, ontology alignment, etc.

Teachers will assign a project to each group 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