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.
Keywords
- Knowledge Representation and Reasoning: Knowledge Representation Languages
- Knowledge Representation and Reasoning: Logics for Knowledge Representation
- Machine Learning: Knowledge-based Learning
- Machine Learning Applications: Applications of Supervised Learning
