Bachelor
2023/2024
Semantic Technologies
Type:
Elective course (Applied Mathematics and Information Science)
Area of studies:
Applied Mathematics and Information Science
Delivered by:
School of Data Analysis and Artificial Intelligence
Where:
Faculty of Computer Science
When:
4 year, 3 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Кикоть Станислав Павлович
Language:
English
ECTS credits:
4
Contact hours:
44
Course Syllabus
Abstract
This course is an introduction to Semantic Technologies that provide easier ways to find, share, reuse and combine information. Semantic Technologies define and link data on the Web or within an enterprise by developing languages to express rich, self-describing interrelations of data in a form that machines can process. They provide an abstraction layer above existing IT technologies that connects data, content and processes. Semantic Technology standards developed by W3C include - a flexible data model RDF (Resource Description Framework) for storing data in graph databases - schema and ontology languages for describing concepts and relationships (RDFS and OWL) - the query language SPARQL designed to query data across various systems and databases and to retrieve and process data stored in RDF format. Applications of Semantic Technologies range from Linked Data, Wikidata, Healthcare and Pharma Industry, Supply Chain Management, Publishing and Media Management, Web Search and E-commerce to Data Integration in the Oil & Gas industry.
Learning Objectives
- introduce the theoretical foundations of Semantic Technologies, including the languages RDF/S, SPARQL, the Web Ontology Language OWL
- provide the students with practical skills of modelling data using RDF/S, querying RDF triplestores, relational databses and XML documents, building ontologies and using datalog
- overview the current applications of Semantic Technologies in health care, media management, and industry;
- demonstrate a few standard algorithms for classification of concepts in ontologies and answering queries
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
- Querying relational databases
- RDF/RDFs, language Turtle
- Query language SPARQL
- Ontology-based data access
- OWL, Ontology engineering
- Reasoning with OWL
- Deductive databases
Bibliography
Recommended Core Bibliography
- Poli R., Healy M., Kameas A. (ed.). Theory and applications of ontology: Computer applications. – New York : Springer, 2010.
Recommended Additional Bibliography
- Stuckenschmidt, H., Van Harmelen, F. Information sharing on the semantic web. – Springer Science & Business Media, 2005.