03360ntm a22005417i 4500 000719381 CZ-PrVSE 20250126140333.0 m d cr n|||||||||| 250126s2024 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Ludvíková, Dominika ISIS:133128 dis Dolování pravidel ze znalostních grafů eng Mining rules from knowledge graphs / Dominika Ludvíková 2024 ?? stran : digital, PDF soubor Vedoucí práce: Vojtěch Svátek Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2025 This thesis explores the topics of knowledge graphs and mining association rules from them. Knowledge graphs are large data structures which store linked data that describe the relations between entities in a semantic way. The massive quantities of data stored in those graphs are a great source of material for data mining analysis. One of the most explainable data mining methods that focuses on finding relationships and patterns is association rule mining. Biochemistry is one of the most progressive fields in terms of publishing linked data. One example of this effort is KG-Microbe which is a knowledge graph that connects data concerning microorganisms and their features and functions. The experiment which is described in this thesis uses a special tool called RDFRules for mining association rules from this knowledge graph. Other experiments have been conducted on the data from this knowledge graph in a flattened format and using less explainable machine learning methods. This work describes the process of creating an association-rule-based classifier to make predictions about the cultivation medium of a microbe. While the accuracy of this classifier is not on par with a referential CatBoost classifier, the results are highly interpretable and can be used for further analysis of the new-found patterns in the graph. Způsob přístupu: Internet znalostní a webové technologie [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas KG-Microbe knowledge graphs association rules data mining RDF linked data RDFRules Svátek, Vojtěch, 1967 prosinec 1.- mzk2004217940 ths Berka, Petr, 1959- jn20001103427 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/84434/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/84434 Hlavní práce https://insis.vse.cz/zp/84434/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/84434/posudek/oponent/84689 Oponentura https://insis.vse.cz/zp/84434/podrobnosti dc:identifier NEPOSILAT VSKP vse84434 250124 84434