Rule mining over linked data - Zeman, Václav (2023)
Rule learning is one way for knowledge discovery from databases. With the growing amount of machine-readable and linked data published on the web, conventional rule mining algorithms needed to be adapted to the properties of the knowledge graphs into which linked data are grouped. Knowledge graphs can contain millions of statements, describe different entities in different contexts, have a high degree of interconnectedness, are considered incomplete, and are therefore open to further dissemination. One of the algorithms capable of efficiently and quickly mining rules from knowledge graphs is AMIE. This algorithm combines various techniques from the field of inductive logic programming and association rule mining. This thesis presents several algorithmic extensions to AMIE, which make it faster and more robust. All proposed approaches have been implemented and encapsulated in a single rule mining system called RDFRules, which can handle many knowledge graphs at once, merge them and analyze input data, mine rules according to the AMIE approach, process output rules, and use them for predictive or descriptive purposes. The performed evaluations show that the proposed improvements of the AMIE algorithm reduce the mining time by up to several orders of magnitude. Furthermore, it has been shown that the newly proposed data pre-processing and rules post-processing methods can discover additional knowledge and provide a better predictive ability of the assembled models than in the case of other common approaches.