Abstract
Inductive Logic Programming (ILP) and Relational Data Mining (RDM) address the task of inducing models or patterns from multi-relational data. One of the established approaches to RDM is propositionalization, characterized by transforming a relational database into a single-table representation. After introducing ILP and RDM, the paper provides an overview of propositionalization algorithms, which have been made publicly available through the web-based ClowdFlows data mining platform. The paper concludes by presenting recent advances in Semantic Data Mining, characterized by exploiting relational background knowledge in the form of domain ontologies in the process of model and pattern construction.
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