Učni načrt predmeta

Predmet:

Tehnologije semantičnega spleta

Course:

Semantic Web Technologies

Študijski program in stopnja / Study programme and level	Študijska smer / Study field	Letnik / Academic year	Semester / Semester
Informacijske in komunikacijske	Tehnologije znanja	1	2
Information and Communication	Knowledge Technologies	1	2

Vrsta predmeta / Course type

Izbirni

Univerzitetna koda predmeta / University course code:

IKT2-708

Predavanja Lectures	Seminar Seminar	Vaje Tutorial	Klinične vaje work	Druge oblike študija	Samost. delo Individ. work	ECTS
15	15			15	105	5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:

prof. dr. Dunja Mladenić

Sodelavci / Lecturers:

Jeziki / Languages:

Predavanja / Lectures:

slovenščina, angleščina / Slovenian, English

Vaje / Tutorial:

Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:

Prerequisites:

Zaključen študijski program prve stopnje s področja naravoslovja, tehnike ali računalništva. disciplines or computer science.

Student must complete first-cycle study programmes in natural sciences, technical

Vsebina:

Content (Syllabus outline):

Osnove tehnologij semantičnega spleta:
- Standardne predstavitve podatkov.
- Definicija ontologije pri semantičnem spletu.
- Primer ontologije.

Tehnike gradnje in analize ontologij:
- vizualizacija podatkov;
- (pol)avtomatska gradnja ontologij;
- evalvacija ontologij.
- napovedovanje strukturnih sprememb pri evoluciji ontologij.

Analiza podatkov z uporabo semantičnih tehnologij:
- praktični primeri,
- semantične tehnologije in generativna umetna inteligenca

Poglobljeno individualno delo na obravnavi realnega primera iz študentovega raziskovalnega dela: izbira primernih podatkov in relevantnega problema, ki ga bo študent obravnaval z metodami semantičnih tehnologij; zbira primernih pristopov za predstavitev podatkov in metod za analizo podatkov.

Introduction to semantic Web technologies:
- standard representations.
- definition of ontology in semantic Web context.
- ontology example.

Construction and analysis of ontologies:
- data visualization;
- (semi)automatic ontology construction;
- ontology evaluation.
- prediction of structural changes in evolution of an ontology.

Data analysis using semantic technologies:
- practical examples,
- semantic technologies and generative AI

In-depth individual study of a real case related to student’s research interests: selection of data sources and a relevant problem to be addressed using semantic technologies; selection of appropriate data representation and suitable data analysis methods.

Temeljna literatura in viri / Readings:

- Journal of Web Semantics, Elsevier.
- Semantic Web (journal), IOS Press.
- GROBELNIK, Marko, MLADENIĆ, Dunja, WITBROCK, Michael J. Text mining for the semantic web. In: C. Sammut,G. Webb Eds. Encyclopedia of machine learning and data mining. Heidelberg [etc.]: Springer. 2017.
- Grigoris Antoniou and Paul Groth. A Semantic Web Primer (Information Systems), 2012. (selected chapters)
- Gerhard Paaß and Sven Giesselbach. Foundation Models for Natural Language Processing: Pre-trained Language Models Integrating Media, Springer, 2023 (selected chapters)

Dodatna literatura:
- Manning, C.D., Schutze, H. (2001). Foundations of Statistical Natural Language Processing, The MIT
Press, Cambridge, MA.

Cilji in kompetence:

Objectives and competences:

Osnovni cilj predmeta je usposobiti študenta, da bo znal uporabiti teoretične osnove s področja tehnologij semantičnega spleta in analize spletnih podatkov, ki jih pridobi pri tem predmetu za reševanje praktičnih problemov s tega področja.

Uvodoma so predstavljene osnovne tehnologije, standardi in predstavitev podatkov. Posebno pozornost posvetimo ontologijam, definiciji, gradnji, evalvaciji in evoluciji le-teh. V drugem delu so predstavljene napredne tehnologije semantičnega spleta s poudarkom na uporabi metod strojnega učenja in odkrivanja zakonitosti v podatkih za potrebe semantičnega spleta. Predstavimo tehnike za analizo spletnih podatkov s poudarkom na vlogi ontologij in semantičnega spleta.

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.

Predvideni študijski rezultati:

Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi
obveznostmi tega predmeta pridobili:
- sposobnost analize, sinteze in predvidevanja rešitev ter posledic,
- obvladanje raziskovalnih metod, postopkov
in procesov, razvoj kritične in samokritične presoje,
- sposobnost uporabe znanja v praksi,
- avtonomnost v strokovnem delu,
- razvoj komunikacijskih sposobnosti in
spretnosti, posebej komunikacije v mednarodnem okolju,
- etična refleksija in zavezanost profesionalni
etiki,
- kooperativnost, delo v skupini (in v
mednarodnem okolju),
- sposobnost identifikacije in analiza problemov ter načrtovanje strategij za njihovo reševanje,
- sposobnost izbire in uporabe ustreznih teorij in programskih orodij s področja tehnologij semantičnega spleta,
- znanja za integracijo tehnologij semantičnega spleta na različna področja.

Students successfully completing this course will
acquire:
- An ability to analyse, synthesise and
anticipate solutions and consequences,
- To gain the mastery over research methods,
procedures and processes, a development of
the critical judgement,
- An ability to apply the theory into practice,
- An autonomy in the professional work,
- Communicational-skills development;
particularly in international environment,
- Ethical reflexion and obligation to professional ethics,
- Cooperativity, team work (in an international environment),
- Ability to analyse problems from the semantic
Web area and plan strategy for addressing them,
- Ability to identify and apply appropriate
technologies and software tools from the area
of semantic Web technologies,
- Knowledge on applications for integration of
semantic Web technologies into different areas.

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno
delo

Lectures, seminar, consultations, individual work

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Seminar

50 %

Seminar

Ustni izpit

50 %

Oral exam

Reference nosilca / Lecturer's references:

1.	REI, Luis, MLADENIĆ, Dunja, DOROZYNSKI, Mareike, ROTTENSTEINER, Franz, SCHLEIDER, Thomas, TRONCY, Raphaël, SEBASTIÁN LOZANO, Jorge, GAITÁN SALVATELLA, Mar. Multimodal metadata assignment for cultural heritage artifacts. Multimedia systems. [Online ed.]. Apr. 2023, vol. 29, iss. 2, str. 847-869, ilustr. ISSN 1432-1882. https://link.springer.com/content/pdf/10.1007/s00530-022-01025-2.pdf, DOI: 10.1007/s00530-022-01025-2.
2.	SITTAR, Abdul, MLADENIĆ, Dunja, GUČEK, Alenka, GROBELNIK, Marko. BAR-analytics : a web-based platform for analyzing information spreading barriers in news : comparative analysis across multiple barriers and events. International journal of information management data insights. Dec. 2025, vol. 5, iss. 2, [article no.] 100368, str. 1-20, ilustr. ISSN 2667-0968. https://www.sciencedirect.com/science/article/pii/S2667096825000503, DOI: 10.1016/j.jjimei.2025.100368.
3.	SWATI, Swati, MLADENIĆ GROBELNIK, Adrian, MLADENIĆ, Dunja, GROBELNIK, Marko. A commonsense-infused language-agnostic learning framework for enhancing prediction of political bias in multilingual news headlines. Knowledge-based systems. [Online ed.]. Oct. 2023, vol. 277, [article no.] 110838, str. 1-15, ilustr. ISSN 1872-7409. https://www.sciencedirect.com/science/article/pii/S0950705123005889?via%3Dihub, DOI: 10.1016/j.knosys.2023.110838. [COBISS.SI-ID 160050179]
4.	REI, Luis, MLADENIĆ, Dunja. Detecting fine-grained emotions in literature. Applied sciences. Jul. 2023, vol. 13, iss. 13, [article no.] 7502, str. 1-26, ilustr. ISSN 2076-3417. https://www.mdpi.com/2076-3417/13/13/7502/htm.
5.	NOVAK, Erik, BIZJAK, Luka, MLADENIĆ, Dunja, GROBELNIK, Marko. Evaluating text generation model performance by combining semantic meaning and word order. IEEE access. 2024, vol. 12, art. 95265, 13 str. ISSN 2169-3536. https://ieeexplore.ieee.org/document/10591977/authors, DOI: 10.1109/ACCESS.2024.3426082. [COBISS.SI-ID 202343939]