Evolutionary learning of existential rules

Abstract

Declarative rules such as Prolog and Datalog are common formalisms to express expert knowledge and are used in a number of systems. Since developing such rules is time consuming and requires scarce expert knowledge, it is essential to develop algorithms for learning such rules. We address the problem of learning existential rules, a richer class of rules which found applications in many use-cases such as Semantic Web and Web Data Extraction. In particular, we concentrate on developing evolutionary learning algorithms for rule learning.