Teaching code

4S008906

Academic staff

Matteo Cristani, Daniele Meli

Coordinator

Matteo Cristani

Credits

6

Also offered in courses:

• Information Technology of the course Master's degree in Linguistics
• Knowledge Representation of the course Master's degree in Artificial intelligence

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

Semester 2 dal Mar 6, 2023 al Jun 16, 2023.

Lessons timetable Moodle 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

Basic knowledge of programming and logic.

Program

1. Elements of Logic
a. Propositional languages
b. First order languages
c. Second order Languages
2. Introduction to computational logic
a. Reasoning tasks
b. Subsumption, satisfiability, consistency, disjointness
3. Structural description logic
a. The FL- language
i. Syntax
ii. Semantics
b. The AL Logic
i. Syntax and semantics
ii. Structural subsumption algorithm
c. ALU, ALE
d. ALN
4. Propositional description logics
a. ALC, ALCN
i. Syntax and semantics
ii. Tableau for ALCN
b. ALCI
c. ALCQIreg
i. Tableau inapplicability
ii. Two-ways alternate automata on infinite trees
5. Description logic systems
a. Protegè/OWL

Bibliography

Didactic methods

Introductory lesson to individual topics, exercises for individual topics and review of a group of topics as scheduled in preparation for the exam.

Learning assessment procedures

The exam is formed by one homework on one of the topics of the course, and possibly in an integrative oral exam. Goal of the homework is to check the learning level of knowledge representation and methods for natural language processing, in the practice of a complex problem. The student can be focusing on one of the two specific themes, and thus we shall agree on an oral integration, on the theme less employed in the homework. The homework shall document the development of a computer program in a language the student is fluent on, implementing solutions based open KR or NLP methods.

Evaluation criteria

Completeness and correctness of the contents of the homework

Criteria for the composition of the final grade

The grade is assigned to the homework

Exam language

Italiano