Rule Discovery in RDF Knowledge-Bases

Friday, August 30, 2019, 11:00 am - 12:00 pm PDTiCal
10th floor conference room (1016)
This event is open to the public.
AI Seminar
Paolo Papotti, EURECOM, Campus Sophia Tech
Video Recording:

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.

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