27311 - Humanistic IT (1)

Learning outcomes

At the end of the course the student understands the concept of information and knows the methods of digital representation and the systems of automatic data processing in the field of humanities; he/she possesses theoretical knowledge, methodological skills and techniques to represent and process humanistic data.

Course contents

The students will master the basics of representation and extraction of information from digital content, intended as data suitable to machine querying and programmatic processing. The course includes the following themes:

Knowledge Graphs design and querying with computational logic methods.
Knowledge Extraction as a hybridization of either rule-based heuristics (scraping, linguistic patterns, graph-based data analysis), or statistical methods (machine learning, generative models) for extracting data from arbitrary content.
Representation and extraction will be presented both from a foundational (philosophical, cognitive) viewpoint, as well as optimal satisfaction of task-oriented requirements.
The Web will be the computational platform to learn, test, and apply the learnt methods, with examples from philosophy, social sciences and humanities.
Some software components will be introduced during the course.

Seminars describing real world computational and semantic methods in the humanities will be featured during the course.

Readings/Bibliography

Teaching material from sessions and seminars, uploaded on Virtuale, including links to tutorials and papers that are available on the Web to fill some background knowledge

Teaching methods

The course will be given in frontal lectures of 2 hours each, possibly including hands-on sessions and guest lectures.

Time permitting, a small project will be implemented by groups of students. Informal contests, such as a SPARQL treasure hunt, will be proposed to students.

Projects and informal contests will contribute to the final grades.

Assessment methods

The final exam will consist of a project work to be presented/defended with the teacher. Students will work in small teams (typically 3 members), choosing a realistic domain or problem that can be modelled, investigated, and evaluated.

The choice will be guided by the teacher, who will also accompany the teams in their work when needed. The work should be fairly spread among the members, and motivated during the exam presentation.

Teaching tools

Besides the teaching facilities installed in the lab, software tools for design, querying, and visualizing knowledge graphs will be used on  existing machines by the students. The tools will enable the students to experiment a likely setting for real world semantic technology projects.

Social media will be also used for informal interaction among students, and with the teacher.

Office hours

See the website of Aldo Gangemi