Introduction to Knowledge Representation

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

Кто читает:

Департамент анализа данных и искусственного интеллекта

Статус:

Курс по выбору

Когда читается:

2-й курс, 1, 2 модуль

Преподаватели

Кикоть Станислав Павлович

Соколов Павел Павлович

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

Assessment Elements

• HW1-XPATH
• HW2-RDF
• HW3-SPARQL
• HW4-DL
• HW5-Ontology
• Экзамен

Interim Assessment

• 2025/2026 2nd module
  0.1 * HW1-XPATH + 0.1 * HW2-RDF + 0.1 * HW3-SPARQL + 0.1 * HW4-DL + 0.1 * HW5-Ontology + 0.5 * Экзамен

Bibliography

Recommended Core Bibliography

• Foundations of semantic Web technologies, Hitzler, P., 2010
• Programming the Semantic Web, Segaran, T., 2009

Recommended Additional Bibliography

• Kripke's worlds : an introduction to modal logics via tableaux, , 2014

Authors

• Антропова Лариса Ивановна
• KIKOT STANISLAV PAVLOVICH