AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

Knowledge Graphs (KGs) are huge collections of primarily encyclopedic facts, which are automatically extracted from the Web. Prominent examples of KGs include Yago, DBPedia, Google Knowledge Graph. We all use KGs when posing simple queries like "capital of Saarland" to Google. Internally, such queries are translated into machine readable representations, which are then issued against the KG stored at the backend of the search engine. Instead of syntactically relevant Web pages, the actual answer to the above query, "Saarbrücken", is then output to the user as a result.

However, since KGs are automatically constructed, they are often inaccurate and incomplete. In this talk, I will investigate how deductive and inductive reasoning services could be used to address these crucially important issues. More specifically, first, I will present an approach for repairing inconsistencies in hybrid logical systems that can be built on top of KGs. Second, I will describe a method for inductive learning of rules with exceptions from KGs and show how these are applied for deriving missing facts.

Jennifer Müller

2900

--email hidden

passcode not visible

logged in users only