Teaching code

4S02793

Teacher

Matteo Cristani

Coordinator

Matteo Cristani

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

II semestre dal Mar 2, 2020 al Jun 12, 2020.

Lessons timetable Moodle Seminars 0

Learning outcomes

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.

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
6. Natural Language Processing
7. Social network analysis and techniques of social network mining

Examination Methods

The exam consists in the preparation of a homework, in the oral discussion of that with further theoretical questions.

The homework shall consist in the implementation of one of the techniques presented in the lectures, particularly one of the following:

- ontology-driven social network crawling;
- ontology-based text analysis.

First part of the oral exam will detail the modalities of abstract solving and implementation on the chosen problem, that will be agreed with each student individually. Behind this, questions will be posed on discipline contents presented in class and discusses in the textbook.

Evaluation of the homework will consider:

- quality of the implemented solution, relatively to the complexity of the problem, with the regular parameters of effectiveness and efficiency in the field of algorithm theory;
- specific usage of formal ontologies, its formalization and complexity, quality in the adoption of design standards with respect to the current methodologies of domain ontology design;
- complexity of the implemented ontology in OWL-DL and its quality, relevance with respect to the implemented technique.

The oral evaluation will consider:

- completeness in answering to questions related to homework;
- competence on the themes specified in the Course Syllabus;
- correctness and amplitude of the answers to the questions.