Master
2024/2025
Introduction to Knowledge Representation
Type:
Elective course (Data Science)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
School of Data Analysis and Artificial Intelligence
Where:
Faculty of Computer Science
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Кикоть Станислав Павлович
Master’s programme:
Data Science
Language:
English
ECTS credits:
6
Course Syllabus
Abstract
Knowledge Representation and Reasoning (KRR) is a field in artificial intelligence (AI)
that focuses on how to represent information in a way that a computer system can
use to make decisions with human-like reasoning.
Knowledge representation involves structuring information in a form that a computer can understand.
A way to do this is by using ontologies or knowledge graphs, which allows for relationships and hierarchies within your data to be represented.
Reasoning refers to the process of drawing conclusions, making inferences, and solving problems based on the information in the knowledge graph. With a reasoning engine, these logical operations can be performed on the represented knowledge to derive new information.
Learning Objectives
- KRR systems are more expressive than traditional databases so make it easier to model complex knowledge.
- Greater flexibility in the way data is structured makes KRR ideal to represent real world relationships and concepts
- Reasoning transforms this data into valuable information, both to the benefit of performance and insights that can be extracted.
Expected Learning Outcomes
- understand and use deductive database systems
- understand and use the ontology language OWL 2 and its profiles
- understand and use the RDF framework and associated technologies such as RDFa and SPARQL
- understand fundamental concepts, advantages and limitations of Semantic Technologies
- understand relational databases and XML documents
- understand the basics of knowledge representation with description logics
- understand the principles of ontology-based data access and integration
Course Contents
- Introduction
- XML/XML Schema, XPath
- RDF/RDFs, language Turtle
- Query language SPARQL
- OWL
- Reasoning with OWL
- Tableau Algorithm for Reasoning in Description Logics
- Ontology engineering
- Deductive databases
- Ontology-based data access
Interim Assessment
- 2024/2025 2nd module0.5 * Exam + 0.1 * HW1-XPATH + 0.1 * HW2-RDF + 0.1 * HW3-SPARQL + 0.1 * HW4-DL + 0.1 * HW5-Ontology
