Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

A.A. 2026/2027 A.A. 2025/2026

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

1° Year

ModulesCreditsTAFSSD
6
B
INF/01
12
B
ING-INF/05
12
B
INF/01

2° Year  It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2025/2026
- 2nd year - AI & cloud - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer vision & deep learning - delivered in 2025/2026 and in 2026/2027
6
B
ING-INF/05
6
B
INF/01
6
B
INF/01
6
B
ING-INF/05
6
B
INF/01
6
B
ING-INF/05
Between the years: 1°- 2°
2 courses among (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2025/2026
- 2nd year - AI & cloud, Visual intelligence - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer Vision & deep learning, Statistical learning - delivered in 2025/2026 and in 2026/2027
6
C
ING-INF/05
6
C
INF/01
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/06
Between the years: 1°- 2°
2 courses among the following
- A.A. 2025/2026 Network Science not activated
- A.A. 2026/2027: Complex Systems not activated
6
C
ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/08
6
C
MAT/09
6
C
INF/01 ,ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/06
6
C
INF/01
Between the years: 1°- 2°
1 course among the following
6
C
SECS-S/06
6
C
IUS/07
6
C
IUS/02
6
C
M-FIL/02
6
C
IUS/08
6
C
IUS/17
6
C
IUS/04
6
C
IUS/01
Between the years: 1°- 2°
Further activities: 3 CFU training and 3 CFU further language skill or 6 CFU training. International students (i.e. students who do not have an Italian bachelor’s degree) must compulsorily gain 3 CFU of Italian language skills (at least A2 level) and 3 CFU training.
6
F
-
Between the years: 1°- 2°
12
D
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Knowledge Representation  (2025/2026)

Teaching code

4S010676

Teacher

Matteo Cristani

Coordinator

Matteo Cristani

Credits

6

Also offered in courses:

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

1st semester dal Oct 1, 2025 al Jan 30, 2026.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

The course’s purpose is to provide the fundamental concepts of knowledge representation both respect to the abstract problem of defining a domain ontology and to the problem of indexing documental domains. Specifically, both logical techniques and machine learning techniques for classification and analysis of documents. At the end of the course the student shall have acquired knowledge bunches on knowledge representation and its applications, and also shall be comfortable with the technical aspects of statistical natural language processing, understanding and connecting both aforementioned aspects while relating them to document repositories, especially the world wide web. These bunches of knowledge shall habilitate the student in: i) building formal ontologies; ii) managing ontology alignement; iii) managing document retrieval with indices based on text content; iv) using formal methods for text analysis while combining these with automated reasoning techniques. At the end of the course the student will be able to: i) presenting a conceptual semantic analysis, describing the process that leads a domain expert to the delivery of information needed by the knowledge engineer to deliver a formal ontology describing the interest domain; ii) going further, potentially autonomously, study and research in the field of semantic technologies in several different application fields.

Prerequisites and basic notions

Basics of Propositional Logic and Predicate Logic

Program

Logic Review Structural Descriptive Logics Propositional Descriptive Logics Algorithms for Subsumption, Coherence and Consistency Systems for Descriptive Logics: Protégé

Bibliography

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Didactic methods

The problems of knowledge representation are presented from both a theoretical and an applicative point of view and solutions are proposed that admit efficient implementations.

Learning assessment procedures

Presentation of a thesis on a topic selected by the student

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

Correctness and completeness

Criteria for the composition of the final grade

Evaluation of the thesis and possible oral integration

Exam language

Inglese