Paolo Papotti
"Robust Discovery of Positive and Negative Rules in Knowledge-Bases"
In this talk I present RuDiK, a system for the discovery of declarative rules over knowledge-bases (KBs). RuDiK discovers rules that express positive relationships between entities, such as “if two persons have the same parent, they are siblings”, and negative rules, i.e., patterns that identify contradictions in the data, such as “if two persons are married, one cannot be the child of the other”. While the former class infers new facts in the KB, the latter class is crucial for other tasks, such as detecting erroneous triples in data cleaning, or the creation of negative examples to bootstrap learning algorithms. RuDiK discovers rules with a more expressive rule language than previous approaches, and its mining is robust to existing errors and incompleteness in the KB. We have conducted extensive experiments using real-world KBs to show that RuDiK outperforms previous proposals in terms of efficiency and that it discovers more effective rules for the application at hand.