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Knowledge Analysis & Management

Description

COURSE OBJECTIVES

Knowledge extraction and representation are key enablers for data comprehension and decision making. This course will cover techniques for the extraction of knowledge from natural language documents and for the representation of knowledge as ontologies and semantic webs.

 

COURSE DESCRIPTION

We will go through text analysis techniques such as part of speech tagging, dependency parsing, text summarization, sentiment analysis, and we will consider knowledge query and representation technologies, such as SPARQL, RDF and OWL

 

LEARNING METHODS

Students will be involved in practical exercises and will experiment with the presented techniques by applying them to the course projects.

 

EXAMINATION INFORMATION
Optional written mid-term exam; final oral exam; optional homework; two mandatory projects.

 

RECOMMENDED COURSES

  • Data Design & Modeling
  • Information Modeling & Analysis

 

REFERENCES
Lecture slides and lecture notes, available on iCorsi

People

 

Tonella P.

Course director

Humbatova N.

Assistant

Additional information

Semester
Fall
Academic year
2021-2022
ECTS
6
Language
English
Education
Master of Science in Software & Data Engineering, Foundation course, 2nd year
PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (4 ECTS)
PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (4 ECTS)