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. 2024/2025 A.A. 2023/2024 A.A. 2022/2023

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea magistrale in Artificial Intelligence - Enrollment from 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
12
B
ING-INF/05
12
B
ING-INF/05
12
B
INF/01
2 modules among the following (A.A. 2024/2025 Network Science 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
1 module 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

2° Year  It will be activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
6
C
INF/01
Final exam
18
E
-
It will be activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
6
C
INF/01
Final exam
18
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud - 1st and 2nd year: Computer Vision & Deep learning)
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 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud, Visual intelligence, Statistical learning - 1st and 2nd year: Computer Vision & Deep Learning)
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°
12
D
-
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
-

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  (2024/2025)

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

Semester 1  dal Oct 1, 2024 al Jan 31, 2025.

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 logic, general concepts of databases.

Program

Elements of predicate logic
Structural descriptive logics
Structural subsumption algorithms
Propositional description logics
Tableaux algorithm
Predicative description logics
Correspondence-based methods with 2ATA

Bibliography

Vai alla bibliografia
Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

Lessons and laboratory

Learning assessment procedures

The exam consists of the implementation of a formal ontology in the Protégé language. Themes will be assigned at the end of the course.

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 of the implementation. Correctness and completeness of the conceptual analysis.

Criteria for the composition of the final grade

Correctness and completeness of the implementation counts 50%. Correctness and completeness of the conceptual analysis counts for 50%.

Exam language

Emglish