Abstract: The Web has become an extremely large source of information and also a platform of various e-service including e-business, e-science, e-learning, e-government, etc. How to develop the new ...

A largely incomplete but hopefully useful list of links to datasets for relational learning and inductive logic programming. No guarantees on availability. Symbolic function approximator aims to ...

Abstract: Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of concept learning specifically ...

Inductive logic programming (ILP) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules from ...

Me and another student at Unibo developed a solution entirely written in Prolog to the game proposed in the following website: https://www.codingame.com/training/hard ...

Empirical methods for building natural language systems has become an important area of research in recent years. Most current approaches are based on propositional learning algorithms and have been ...

Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional ...