Semantical Data Analysis

Major: Computer Sciences
Code of subject: 7.122.00.M.22
Credits: 5.00
Department: Artificial Intelligence Systems
Lecturer: Boyko Nataliya
Semester: 2 семестр
Mode of study: денна
Learning outcomes: 1. Application of constructive methods of mathematical linguistics: information theory and information systems; set theory, discrete mathematics and combinatorics, methods of mathematical statistics and probability theory, terminology and definitions of basic concepts of information technologies to the extent sufficient for the application of the basic knowledge, skills and skills acquired for practical application in the processing of various text materials. 2. Ability to create imitation datasets for testing methods and debugging algorithms, graphically presenting the results of the use of information technologies of word processing. 3. Use of effective methods of analysis and visualization of text data in modern information systems.
Required prior and related subjects: Machine learning
Summary of the subject: Elements of information theory in linguistics. Probabilistic text modeling. Information on linguistic events. Statistical linguistics. Identification of texts
Assessment methods and criteria: - current control (40%): written reports on laboratory work, practical tasks, oral examination; - final control (60% of exam), testing (50%), oral component (10%).
Recommended books: 1. Darchuk N.P. Computer linguistics (automatic text processing): textbook / N.P. Darchuk. - K.: VOC "Kyiv University", 2008. - 351 p. 2. Perebijnis V.I. Mathematical linguistics. - K.: Ed. KNLU Center, 2014. - 125 p. 3. Karpilovska E.A. Introduction to applied linguistics: computer linguistics: Textbook.— Donetsk: Yugo-Vostok, Ltd. LLC, 2006.— 188 p. 4. Berners-Lee T., Hendler J., Lassila O. The Semantic Web. Scientific American, May 2001. 5. Benjamins V. Fensel D, Decker S., Gemez-Perez A. (KA)2: Building Ontologies for the Internet // a Mid Term Report. - 1999. 6. Borgida A., Brachman R., McGuiness D., Resnick L. Classic: A structural data model for objects // ACM SIGMOID Int. Conf. on Management of Data, Portland, Oregon, USA, - 1989. 7. Brachman R., Schmolze J. An Overview of the KL-ONE Knowledge Representation System // Cognitive Science, - Vol. 9, - No. 2, - 1985.- P.171-216. 8. Bray T., Paoli J., Sperberg C. Extensible Markup Language (XML) 1.0. W3C Recommendation. - Feb 1998. - http://www.w3.org/TR/RECxml. 9. Manola F., Miller E. RDF Primer. October 2003. - http://www.w3.org/TR/rdf-primer/. 10. OKBC: A Programmatic Foundation for Knowledge Base Interoperability. V. Chaudhri, A. Farquhar, R. Fikes, P. Karp, J. Rice // Fifteenth National Conf. on Artificial Intelligence. AAAI98, AAAIPres/The MIT Press, Madison. - 1998- P.600-607. 11. Chalupsky H, OntoMorph: A translation system for symbolic knowledge // In: Cohn A.G., Giunchiglia F., Selman B. (Eds.), Principles of Knowledge Representation and Reasoning // Proc. of the Seventh Intern. Conf. (KR2000). Morgan Kaufmann Publishers, San Francisco, CA. 12. The Description Logic Handbook. Theory, Implementation and Applications. Edited by F. Baader, D. Calvanese, D. McGuinness, D. Nardi, Peter Patel-Schneider, Cambridge - 2003 - 574 pages. 13. Doan A., Madhavan J., Domingos P., Halevy A. Learning to map between ontologies on the Semantic Web // In: The Eleventh Intern. WWW Conference. Hawaii, US. - 2002. - doan02learning.pdf 14. Dou D., McDermott D., Qi P., Ontology translation by ontology merging and automated reasoning // EKAW'02 workshop on Ontologies for Multi-Agent Systems. SigЁuenza, Spain. - 2002.