Engineering of Data and Knowledge

Major: Information Systems and Technologies
Code of subject: 7.126.01.O.001
Credits: 5.00
Department: Information Systems and Networks
Lecturer: D., Professor Lytvyn Vasyl Volodymyrovych
Semester: 1 семестр
Mode of study: денна
Мета вивчення дисципліни: Data mining techniques. Theoretical aspects of knowledge extraction. Psychological aspect. Linguistic aspect. Epistemological aspect of knowledge extraction. Structuring methods. Evolution of knowledge acquisition systems. Methods of knowledge acquisition. Domain. Domain description language. Semiotic model of domain. Strategies of knowledge acquisition. Methods of knowledge classification and systematization. Theoretical aspects of knowledge structuring. Hierarchical approach. Traditional structuring methodology. Object-structural approach. Methods of knowledge compilation. Communicative methods. Passive methods. Active individual methods. Active batch methods. Textual methods. Methods of structuring. Latent knowledge structure. Semantic space and graduation. Identification of "hidden" knowledge structures . Repertory grid technique. Construct detection method. Repertory grid analysis.
Завдання: Data mining techniques. Theoretical aspects of knowledge extraction. Psychological aspect. Linguistic aspect. Epistemological aspect of knowledge extraction. Structuring methods. Evolution of knowledge acquisition systems. Methods of knowledge acquisition. Domain. Domain description language. Semiotic model of domain. Strategies of knowledge acquisition. Methods of knowledge classification and systematization. Theoretical aspects of knowledge structuring. Hierarchical approach. Traditional structuring methodology. Object-structural approach. Methods of knowledge compilation. Communicative methods. Passive methods. Active individual methods. Active batch methods. Textual methods. Methods of structuring. Latent knowledge structure. Semantic space and graduation. Identification of "hidden" knowledge structures . Repertory grid technique. Construct detection method. Repertory grid analysis.
Learning outcomes: • major techniques of knowledge and data engineering; • methods of knowledge extraction; • linguistic means of knowledge and data description.
Required prior and related subjects: • Machine Learning; • Methods of Natural Language Processing.
Summary of the subject: Data mining techniques. Theoretical aspects of knowledge extraction. Psychological aspect. Linguistic aspect. Epistemological aspect of knowledge extraction. Structuring methods. Evolution of knowledge acquisition systems. Methods of knowledge acquisition. Domain. Domain description language. Semiotic model of domain. Strategies of knowledge acquisition. Methods of knowledge classification and systematization. Theoretical aspects of knowledge structuring. Hierarchical approach. Traditional structuring methodology. Object-structural approach. Methods of knowledge compilation. Communicative methods. Passive methods. Active individual methods. Active batch methods. Textual methods. Methods of structuring. Latent knowledge structure. Semantic space and graduation. Identification of "hidden" knowledge structures . Repertory grid technique. Construct detection method. Repertory grid analysis.
Опис: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% of exam): in written, verbally.
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% of exam): in written, verbally.
Критерії оцінювання результатів навчання: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% of exam): in written, verbally.
Recommended books: • Литвин В. В. Методи та засоби інженерії даних та знань / В. В. Литвин // навчальний посібник з грифом МОНУ. – Львів : «Магнолія-2006», 2012. – 241 с. • Глибовець М.М., Олецький О.В. Штучний інтелект. – К.: КМ Академія, 2002. –