June 7, 2019, 2:00 PM, Room 03/124, Bat5
Title: Rule Discovery in RDF Knowledge-Bases
Abstract: In this talk we present RuDiK, a system for the discovery of declarative rules over RDF 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 report experiments over real-world KBs to show that RuDiK outperforms previous proposals in terms of efficiency and that it discovers more effective rules for data curation. Finally, we discuss how automatically discovered rules can support other applications, such as computational fact checking.