OVERVIEW

The Data Semantics course provides an introduction to the most challenging issues in knowledge and data representation and semantics, with particular emphasis on languages and technologies for the semantic web.

AIMS AND CONTENT

LEARNING OUTCOMES

Students will learn key elements in data protection and privacy: data privacy and anonymity, metrics and techniques; macro and microdata protection; data protection in outsourcing scenarios; privacy on the web; advanced access control. Students will be involved in project activities.

AIMS AND LEARNING OUTCOMES

After the course students will be able to design and implement an ontology and to understand, present and discuss in a critical way the most challenging issues in ontology development and in Natural Language Processing.

TEACHING METHODS

Traditional: frontal lessons and laboratories

SYLLABUS/CONTENT

Contents:

Ontology languages and tools:

• RDF&RDFS
• OWL
• SPARQL

Semantic Data Management: 

• RDF Use Cases
• Linked Open Data
• Ontologies for data integration

Computational linguistic:

• Tools and resources for NLP
• Multilinguality issues
• Open Research problems (negation, irony detection, ...)

Ontology development:

• Ontology engineering
• Ontology learning & population
• Ontology matching

Applications

RECOMMENDED READING/BIBLIOGRAPHY

S. Abiteboul, I. Manolescu, F. Rigaux, M.C. Roust, P. Senellart. Web Data Management. Cambridge University Press. 2011

TEACHERS AND EXAM BOARD

Exam Board

VIVIANA MASCARDI (President)

BARBARA CATANIA

GIOVANNA GUERRINI

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam will consist in a written part (traditional open/closed questions, exercises) plus an individual project (requiring about 1 man/week to be completed).

Each student will choose her/his most preferred project type and topic: as an example, a project might consist in writing a report on issues that have not been discussed in details during the course, or developing a SW component using one of the tools studied during the course, or experimenting with some new SW library/application and reporting the results of the experimentation to the teachers in a written document. In case of SW development, a short report accompanying the developed component is required. 

ASSESSMENT METHODS

The acquisition of the skills foreseen by this course will be assessed via the written exam + the project which have been carefully designed to allow the teachers to verify whether a student is actually able to design and implement an ontology and to understand, present and discuss in a critical way the most challenging issues in ontology development and in Natural Language Processing.

Exam schedule