03245ntm a22005417i 4500 000719535 CZ-PrVSE 20250126140333.0 m d cr n|||||||||| 250126s2024 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Hrudková, Kateřina ISIS:146044 dis Datové transformace pro relační učení eng Data transformations for relational learning / Kateřina Hrudková 2024 ?? stran : digital, PDF soubor Vedoucí práce: Tomáš Kliegr Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2025 In response to the growing need for analysis of relational and graph data, it is necessary to ensure interoperability between different machine learning methods that process this type of data. Among the most important is inductive logic programming (ILP), which is based on the representation of relational data in a form derived from the Prolog language and learning rules from the W3C RDF standard. This thesis focuses on the ILP system Popper and the RDFRules system, which implements the AMIE approach for the analysis of RDF data. At the time of writing this thesis, there were no publicly available tools that would allow conversion between these formats; that is, if the input data was in RDF format, it was not possible to easily import it into the Popper system and vice versa. This thesis, therefore, describes two new libraries programmed in Python that allow users to transform Prolog atoms to RDF triples and vice versa. The tools are designed to support n-ary predicates and both positive and negative examples in ILP representation. The converters were evaluated on four datasets, including Michalski’s trains dataset. Both libraries are publicly available on GitHub. Způsob přístupu: Internet znalostní a webové technologie [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas inductive logical programming, Python RDF rule mining Popper relational learning relational data transformations RDFRules Kliegr, Tomáš ISIS:8484 ths Svátek, Vojtěch, 1967 prosinec 1.- mzk2004217940 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/87829/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/87829 Hlavní práce https://insis.vse.cz/zp/87829/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/87829/posudek/oponent/84687 Oponentura https://insis.vse.cz/zp/87829/podrobnosti dc:identifier NEPOSILAT VSKP vse87829 250121 87829