Rule Mining in RDF Knowledge Bases

The continuous progress of Information Extraction (IE) techniques has led to the construction of large Knowledge Bases (KBs) containing facts about millions of entities such as people, organizations and places. KBs are important nowadays because they allow computers to understand the real world and are used in multiple domains and applications. Furthermore, the discovery of useful and non-trivial patterns in KBs, known as rule mining, opens the door for multiple applications in the areas of data analysis, link prediction and automatic data engineering. In this talk I introduce AMIE, a system that learns logical rules on RDF KBs operating under the Open World Assumption. The scale of current KBs as well as their inherent incompleteness and noise make this endeavor challenging. In addition I show how rule mining can be applied to multiple data analysis and maintenance tasks.