This course introduces the fundamentals of Knowledge Graphs, Ontologies and Lexical Semantics, emphasizing their roles in Natural Language Processing and Artificial Intelligence. It covers the structure and applications of Knowledge Graphs, the function and domains of Ontologies, and the use of semantic-lexical resources. The course explores how these elements interrelate within
computational linguistics and relate to the use of Large Language Models. Students will complete assignments in text annotation, system surveys with presentations, and statistical analysis of relevant resources.
Last update: Mírovský Jiří, RNDr., Ph.D. (20.05.2025)
Kurz seznámí studenty se základy znalostních grafů, ontologií a lexikální sémantiky se zaměřením na jejich roli ve zpracování přirozeného jazyka (NLP) a v umělé inteligenci (AI). Pokrývá strukturu a využití znalostních grafů, funkci a oblasti použití ontologií a využití sémanticko-lexikálních zdrojů. Kurz se také zabývá vzájemnými vztahy těchto zdrojů v rámci počítačové lingvistiky a jak
je lze spojit s využitím velkých jazykových modelů (LLM). Studenti během kurzu vypracují úkoly zahrnující anotaci textů, přhlednou prezentaci různých systémů a statistickou analýzu vybraných zdrojů.
Last update: Mírovský Jiří, RNDr., Ph.D. (20.05.2025)
Course completion requirements -
3 assignments of the following types:
o Test annotation of texts (lexical, textual)
o Survey (with presentation) of various lexical semantic/ontological/KG systems
o Statistical analysis (using existing tools) of a selected resource/topic
Last update: Mírovský Jiří, RNDr., Ph.D. (20.05.2025)
3 domácí úkoly následujícího typu:
o Testovací anotace textů (lexikální, textová)
o Přehled (s prezentací) různých systémů lexikální sémantiky/ontologií/znalostních grafů
o Statistická analýza (s využitím dostupných nástrojů) vybraného zdroje/tématu
Last update: Mírovský Jiří, RNDr., Ph.D. (20.05.2025)
Literature -
Schneider, P., Schopf, T., Vladika, J., Galkin, M., Simperl, E., & Matthes, F. (2022). A Decade of Knowledge Graphs in Natural Language Processing: A Survey. arXiv preprint arXiv:2210.00105.
This paper systematically reviews the integration of knowledge graphs in NLP over the past decade.
Poole, D. L., & Mackworth, A. K. (2023). Knowledge Graphs and Ontologies. In Artificial Intelligence (pp. 701-730). Cambridge University Press. Also at https://artint.info/3e/html/ArtInt3e.Ch16.html.
This textbook chapter explores methods for representing knowledge using ontologies.
Navigli, R., & Ponzetto, S. P. (2012). BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network. Artificial Intelligence, 193, 217-250.
BabelNet is a multilingual lexical-semantic knowledge graph that integrates WordNet and Wikipedia, providing a rich resource for NLP applications.
Palmer, M., Bonial, C., Hwang, J. D. (2017). VerbNet: Capturing English verb behavior, meaning and usage. The Oxford Handbook of Cognitive Science, ed. Susan Chipman. Oxford University Press.
Essential reading on one the first Lexical Semantic Resources focused on events, VerbNet.
Uresova, Z., Fucikova, E., Hajicova, E., Hajic J. (2020). SynSemClass Linked Lexicon: Mapping Synonymy between Languages. In Proceedings of the 2020 Globalex Workshop on Linked Lexicography, pages 10–19, Marseille, France. ELRA.
Multlingual Ontologies are essential for language understanding. SynSemClass focuses on eventives and describes them together with a hierarchy between the concepts.
Baker, C. (2014). FrameNet: A Knowledge Base for Natural Language Processing. In: Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014).
Overview of the FrameNet project for frame semantics.
Van Gysel, J.E.L., Vigus, M., Chun, J. et al. (2021). Designing a Uniform Meaning Representation for Natural Language Processing. Künstl Intell 35, 343–360. https://doi.org/10.1007/s13218-021-00722-w.
Article about a meaning representation for natural language texts that uses various lexical semantic resources.
Last update: Mírovský Jiří, RNDr., Ph.D. (20.05.2025)
Schneider, P., Schopf, T., Vladika, J., Galkin, M., Simperl, E., & Matthes, F. (2022). A Decade of Knowledge Graphs in Natural Language Processing: A Survey. arXiv preprint arXiv:2210.00105.
This paper systematically reviews the integration of knowledge graphs in NLP over the past decade.
Poole, D. L., & Mackworth, A. K. (2023). Knowledge Graphs and Ontologies. In Artificial Intelligence (pp. 701-730). Cambridge University Press. Also at https://artint.info/3e/html/ArtInt3e.Ch16.html.
This textbook chapter explores methods for representing knowledge using ontologies.
Navigli, R., & Ponzetto, S. P. (2012). BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network. Artificial Intelligence, 193, 217-250.
BabelNet is a multilingual lexical-semantic knowledge graph that integrates WordNet and Wikipedia, providing a rich resource for NLP applications.
Palmer, M., Bonial, C., Hwang, J. D. (2017). VerbNet: Capturing English verb behavior, meaning and usage. The Oxford Handbook of Cognitive Science, ed. Susan Chipman. Oxford University Press.
Essential reading on one the first Lexical Semantic Resources focused on events, VerbNet.
Uresova, Z., Fucikova, E., Hajicova, E., Hajic J. (2020). SynSemClass Linked Lexicon: Mapping Synonymy between Languages. In Proceedings of the 2020 Globalex Workshop on Linked Lexicography, pages 10–19, Marseille, France. ELRA.
Multlingual Ontologies are essential for language understanding. SynSemClass focuses on eventives and describes them together with a hierarchy between the concepts.
Baker, C. (2014). FrameNet: A Knowledge Base for Natural Language Processing. In: Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014).
Overview of the FrameNet project for frame semantics.
Van Gysel, J.E.L., Vigus, M., Chun, J. et al. (2021). Designing a Uniform Meaning Representation for Natural Language Processing. Künstl Intell 35, 343–360. https://doi.org/10.1007/s13218-021-00722-w.
Article about a meaning representation for natural language texts that uses various lexical semantic resources.
Last update: Mírovský Jiří, RNDr., Ph.D. (20.05.2025)
Syllabus -
Students will learn the basics of Knowledge Graphs, Ontologies and their relation to Lexical Semantics and other lexical resources used in Natural Language Processing and Artificial Intelligence.
Course Sections:
1. Introduction to the course, wider context (Computational Linguistics research, Natural Language Processing, AI, Large Language Models, Applications)
3. Ontologies – definitions, relation to language, examples in various domains (linguistics, medicine and health, …)
4. Lexical Semantics and Dictionaries – definitions, examples, use in linguistics and language sources
5. Relation between Lexical resources, Ontologies, Knowledge Graphs with focus on NLP and AI tasks
Last update: Mírovský Jiří, RNDr., Ph.D. (20.05.2025)
Studenti se seznámí se základy znalostních grafů, ontologií a jejich vztahem k lexikální sémantice a dalším lexikálním zdrojům využívaným v oblasti zpracování přirozeného jazyka (NLP) a umělé inteligence (AI).
Témata kurzu:
1. Úvod do kurzu, širší kontext (výzkum v oblasti výpočetní lingvistiky, zpracování přirozeného jazyka, umělá inteligence, velké jazykové modely, aplikace)
