Type of studies | Title |
---|---|
Master Academic Studies | Information and Analytics Engineering (Year: 1, Semester: Winter) |
Master Academic Studies | Artificial Intelligence and Machine Learning (Year: 1, Semester: Winter) |
Master Academic Studies | Production Engineering (Year: 1, Semester: Winter) |
Master Academic Studies | Computing and Control Engineering (Year: 1, Semester: Winter) |
Master Academic Studies | Information Engineering (Year: 1, Semester: Winter) |
Master Academic Studies | Software Engineering and Information Technologies (Year: 1, Semester: Winter) |
Category | Theoretical-methodological |
Scientific or art field | Applied Computer Science and Informatics |
ECTS | 6 |
Students gain knowledge of the concepts, techniques and selected examples of semantic web applications.
The acquired knowledge enable the implementation of software systems which support intelligent selection, approach and processing of information on the Web.
Introduction: Structure syntax and semantics. Need for semantics on the Web. Meta-programming, meta-data, XML, XSLT, RDF. Semantics, Semantics and knowledge, Ontologies, Logics, Deduction, Domain modelling, Context, Distributed knowledge. Classification. Knowledge based protocols. Technologies. Ontology tools, Ontology software (API). OWL. SPARQL. Methodologies. Methodologies for ontology engineering. Methodologies for introducing knowledge management systems. Methodologies of developing semantic systems. Semantic systems. Semantic Web services. Semantic Web Portals. Semantic Wiki. Semantic Multi-Agent Systems. Semantic Web Browsers. Applications: bioinformatics, document management systems, information search, etc.
Teaching methods include: lectures, computer practice classes, homework assignments and consultations. During the lectures the content of the course is presented using the necessary didactic tools while student active participation is encouraged. The practical aspect of the course is covered at computer practice classes through assignments which students do independently or with the help of teaching assistants as well as through homework assignments (obligatory or optional). A student is expected to demonstrate the ability of independent task solving or understanding of the solution. The evaluation is in the form of oral conversation with the teaching assistant. The course lecturer and assistant have consultations with the students. During the consultations the students are given additional explanations of the material covered at the lecture and practice classes, and in case the consultations relating to independent work on laboratory or homework tasks, the suggestions are given on how to improve the solutions the students are obliged to provide.
Authors | Title | Year | Publisher | Language |
---|---|---|---|---|
2003 | English | |||
2011 | English | |||
2008 | Ontos, Franfurkt | English | ||
2004 | English | |||
2018 | English | |||
2014 | English | |||
2002 | English | |||
2014 | English |
Course activity | Pre-examination | Obligations | Number of points |
---|---|---|---|
Complex exercises | Yes | Yes | 10.00 |
Theoretical part of the exam | No | Yes | 30.00 |
Lecture attendance | Yes | Yes | 5.00 |
Computer exercise attendance | Yes | Yes | 5.00 |
Project | Yes | Yes | 50.00 |
Full Professor
Full Professor
Assistant - Master
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