MPŠ
MPŠ MP&Scaron MP&Scaron MP&Scaron Avtorji

Jožef Stefan
International
Postgraduate School

Jamova 39
SI-1000 Ljubljana
Slovenia

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10
Email: info@mps.si

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Course Description

Semantic Web Technologies

Program

Information and Communication Technologies, second-level study programme

Lecturers:

prof. dr. Dunja Mladenić

Goals:

The main objective of this course is to provide an overview of semantic Web technologies and analysis of Web data. The course introduces basic theoretical background and technologies and illustrates their usage in practical setting. The study of semantic Web technologies focuses on basic technologies, standards and data representation. As ontologies pay a central role in semantic Web, their definition, construction, evaluation and evolution is addressed in details.
The advanced technologies include usage of machine learning and knowledge discovery methods and connect semantic Web technologies with analysis of semantic Web in general and in particular in connection to ontologies and semantic Web.

Content:

1) Introduction to semantic Web technologies Standard representations. Defnition of ontology in semanitc Web context. Ontology example – Cyc.

2) Construction and analysis of ontologies
Data visualization; (semi)automatic ontology
construction; ontology evaluation. Prediction of structural changes in evolution of an ontology.

3) Web mining and semantic Web
Data representation. Techniques for mining Web content, Web structure and access to Web data. Ontology construction from Web data.

Course literature:

• DAVIES, J., STUDER, Rudi, WARREN, Paul (eds.) Semantic Web Technologies: Trends and Research in Ontology-based Systems. Chichester: John Wiley & Sons, 2006.
• Antoniou, Grigoris, van Harmelen Frank. A Semantic Web Primer (Cooperative Information Systems), MIT Press, Cambridge, MA, 2005.
• Berendt, B., Hotho, A., Mladenić, D., Someren, M.W. Van, Stumme, G., (eds.), Web Mining : From Web to Semantic Web, Lecture notes in artificial inteligence, Lecture notes in computer science, vol. 3209, Berlin; Heidelberg; New York: Springer, 2004.
• Berendt, B., Semeraro, G., Hotho, A., Mladenić, D. (eds.), From Web to Social Web: Discovering and deploying user and content profiles. WebMine 2006. Lecture notes in artificial inteligence, Lecture notes in computer science, Berlin; Heidelberg; New York: Springer, 2007.

Dodatna literatura

• Manning, C.D., Schutze, H. (2001). Foundations of Statistical Natural Language Processing, The MIT Press, Cambridge, MA.
• Mitchell, T.M. (1997). Machine Learning. The McGraw-Hill Companies, Inc.
• Hastie, T., Tibshirani, R. and Friedman, J. H. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, Springer Verlag.

Significant publications and references:

• NOVALIJA, Inna, MLADENIĆ, Dunja. Applying semantic technology to business news analysis. Applied artificial intelligence, ISSN 0883-9514, 2013, vol. 27, no. 6, str. 520-550, doi: 10.1080/08839514.2013.805600.
• MORARU, Alexandra, MLADENIĆ, Dunja. A framework for semantic enrichment of sensor data. V: LUŽAR - STIFFLER, Vesna (ur.), JAREC, Iva (ur.), BEKIĆ, Zoran (ur.). Proceedings of the ITI 2012, (CIT = Jounal of computing and information technology, ISSN 1330-1136). Zagreb: University of Zagreb: University Computing Centre, cop. 2012, vol. 20, no. 3, str. 167-173, doi: 10.2498/cit.1002093.
• GROBELNIK, Marko, MLADENIĆ, Dunja, FORTUNA, Blaž. Semantic technology for capturing communication inside an organisation. IEEE internet computing, ISSN 1089-7801, 2009, vol. 13, no. 4, str. 59-66. [COBISS.SI-ID 22843175]
• TOMAŠEV, Nenad, BUZA, Krisztian, MLADENIĆ, Dunja. Correcting the hub occurrence prediction bias in many dimensions. Computer science and information systems, ISSN 1820-0214. [Print ed.], 2016, vol. 13, no. 1, str. 1-21, doi: 10.2298/CSIS140929039T.
• KARLOVČEC, Mario, MLADENIĆ, Dunja, GROBELNIK, Marko, JERMOL, Mitja. Conceptualization of science using collaboration and competences. Electronic library, ISSN 0264-0473, 2016, vol. 34, no. 1, str. 2-23, doi: 10.1108/EL-01-2014-0015.

Examination:

Seminar and oral exam (100%)

Students obligations:

Seminar and oral exam

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